Wissenschaftliche Artikel

Huymajer, M., Filzmoser, P., Mazak, A., Winkler, L., & Kraxner, H. (2025). Opportunities and pitfalls of regression algorithms for predicting the residual value of heavy equipment — A comparative analysis. Engineering Applications of Artificial Intelligence, 141, 1–13. https://doi.org/10.1016/j.engappai.2024.109599 ( reposiTUm)

Brandl, M., Martinez Sevilla, M. del C., Hauzenberger, C. A., Filzmoser, P., Milić, B., & Horejs, B. (2025). Unveiling Neolithic Economic Behavior: A Novel Approach to Chert Procurement at Çukuriçi Höyük, Western Anatolia. Journal of Archaeological Method and Theory, 32(1), Article 16. https://doi.org/10.1007/s10816-024-09681-6 ( reposiTUm)

Mühlmann, C., Filzmoser, P., & Nordhausen, K. (2024). Spatial Blind Source Separation in the Presence of a Drift. Austrian Journal of Statistics, 53(2), 48–68. https://doi.org/10.17713/ajs.v53i2.1668 ( reposiTUm)

Neubauer, L., & Filzmoser, P. (2024). Improving forecasts for heterogeneous time series by “averaging”, with application to food demand forecasts. International Journal of Forecasting, 40(4), 1622–1645. https://doi.org/10.1016/j.ijforecast.2024.02.002 ( reposiTUm)

Varmuza, K., & Filzmoser, P. (2024). Adjusted Pareto Scaling for Multivariate Calibration Models. Journal of Chemometrics, 38(11), Article e3588. https://doi.org/10.1002/cem.3588 ( reposiTUm)

Brune, B., Ortner, I., & Filzmoser, P. (2024). A rank-based estimation method for mixed effects models in the presence of outlying data. Journal of Data Science, Statistics, and Visualisation, 4(7). https://doi.org/10.52933/jdssv.v4i7.112 ( reposiTUm)

Oguamalam, J., Radojičić, U., & Filzmoser, P. (2024). Minimum Regularized Covariance Trace Estimator and Outlier Detection for Functional Data. Technometrics, 66(4), 588–599. https://doi.org/10.1080/00401706.2024.2336542 ( reposiTUm)

Puchhammer, P., Kalubowila, C., Braus, L., Pospiech, S., Sarala, P., & Filzmoser, P. (2024). A performance study of local outlier detection methods for mineral exploration with geochemical compositional data. Journal of Geochemical Exploration, 258, Article 107392. https://doi.org/10.1016/j.gexplo.2024.107392 ( reposiTUm)

Cappello, C., Piccolotto, N., Mühlmann, C., Bögl, M., Filzmoser, P., Miksch, S., & Nordhausen, K. (2024). Visual Interactive Parameter Selection for Temporal Blind Source Separation. Journal of Data Science, Statistics, and Visualisation, 4(3). https://doi.org/10.52933/jdssv.v4i3.82 ( reposiTUm)

May, D., Bonelli, J., Feuchtner, M., Filzmoser, P., Prat, E. H., & Kummer, S. (2024). Symptom-Score des Sterbeprozesses zur Prognosebeurteilung des Sterbeprozesses bei Bewohnern in Pflegeheimen. HeilberufeSCIENCE, 15(3–4), 95–103. https://doi.org/10.1007/s16024-024-00412-1 ( reposiTUm)

Nesrstová, V., Wilms, I., Hron, K., & Filzmoser, P. (2024). Identifying Important Pairwise Logratios in Compositional Data with Sparse Principal Component Analysis. Mathematical Geosciences. https://doi.org/10.1007/s11004-024-10159-0 ( reposiTUm)

Anna-Christina Glock, Sobieczky, F., Fürnkranz, J., Filzmoser, P., & Jech, M. (2024). Predictive change point detection for heterogeneous data. NEURAL COMPUTING & APPLICATIONS, 36(26), 16071–16096. https://doi.org/10.1007/s00521-024-09846-0 ( reposiTUm)

Kurnaz, F. S., & Filzmoser, P. (2023). Robust and sparse multinomial regression in high dimensions. Data Mining and Knowledge Discovery, 37(4), 1609–1629. https://doi.org/10.1007/s10618-023-00936-6 ( reposiTUm)

Pfeiffer, P., & Filzmoser, P. (2023). Robust statistical methods for high-dimensional data, with applications in tribology. Analytica Chimica Acta, 1279(341762). https://doi.org/10.34726/5289 ( reposiTUm)

Heiler, G., Hanbury, A., & Filzmoser, P. (2023). The Impact of COVID-19 on Relative Changes in Aggregated Mobility Using Mobile-phone Data. Austrian Journal of Statistics, 52(4), 163–179. https://doi.org/10.17713/ajs.v52i4.1510 ( reposiTUm)

Rieser, C., & Filzmoser, P. (2023). Extending compositional data analysis from a graph signal processing perspective. Journal of Multivariate Analysis, 198, Article 105209. https://doi.org/10.1016/j.jmva.2023.105209 ( reposiTUm)

Rieser, C., Fačevicová, K., & Filzmoser, P. (2023). Cell-wise robust covariance estimation for compositions, with application to geochemical data. Journal of Geochemical Exploration, 253, Article 107299. https://doi.org/10.1016/j.gexplo.2023.107299 ( reposiTUm)

Kurnaz, F. S., & Filzmoser, P. (2023). enetLTS: robust and sparse methods for high dimensional linear, binary, and multinomial regression. Journal of Open Source Software, 8(82), Article 4773. https://doi.org/10.21105/joss.04773 ( reposiTUm)

Puchhammer, P., & Filzmoser, P. (2023). Spatially Smoothed Robust Covariance Estimation for Local Outlier Detection. Journal of Computational and Graphical Statistics. https://doi.org/10.1080/10618600.2023.2277875 ( reposiTUm)

Piccolotto, N., Bogl, M., Muehlmann, C., Nordhausen, K., Filzmoser, P., Schmidt, J., & Miksch, S. (2023). Data Type Agnostic Visual Sensitivity Analysis. IEEE Transactions on Visualization and Computer Graphics. https://doi.org/10.1109/TVCG.2023.3327203 ( reposiTUm)

Nesrstová, V., Wilms, I., Palarea‐Albaladejo, J., Filzmoser, P., Martín‐Fernández, J. A., Friedecký, D., & Hron, K. (2023). Principal balances of compositional data for regression and classification using partial least squares. Journal of Chemometrics, 37(12), Article e3518. https://doi.org/10.1002/cem.3518 ( reposiTUm)

Drastichová, M., Filzmoser, P., & Gajanin, R. (2023). Relationships between wellbeing and sustainable development in a group of selected developed countries. Problemy Ekorozwoju, 18(2), 49–77. https://doi.org/10.35784/preko.3941 ( reposiTUm)

Fačevicová, K., Filzmoser, P., & Hron, K. (2022). Compositional cubes: a new concept for multi-factorial compositions. Statistical Papers. https://doi.org/10.1007/s00362-022-01350-8 ( reposiTUm)

Piccolotto, N., Bögl, M., Muehlmann, C., Nordhausen, K., Filzmoser, P., & Miksch, S. (2022). Visual Parameter Selection for Spatial Blind Source Separation. Computer Graphics Forum, 41(3), 157–168. https://doi.org/10.1111/cgf.14530 ( reposiTUm)

Weltler, P., Rappersberger, K., Filzmoser, P., & Vujic, I. (2022). The impact of the COVID‐19 pandemic on melanoma diagnoses. JEADV Clinical Practice, 1(2), 122–125. https://doi.org/10.1002/jvc2.15 ( reposiTUm)

Piccolotto, N., Bögl, M., Gschwandtner, T., Muehlmann, C., Nordhausen, K., Filzmoser, P., & Miksch, S. (2022). TBSSvis: Visual analytics for temporal blind source separation. Visual Informatics, 6(4), 51–66. https://doi.org/10.1016/j.visinf.2022.10.002 ( reposiTUm)

Sarala, P., Lunkka, J. P., Sarajärvi, V., Sarala, O., & Filzmoser, P. (2022). Timing of glacial - non-glacial stages in Finland: An exploratory analysis of the OSL data. Arctic, Antarctic, and Alpine Research, 54(1), 428–442. https://doi.org/10.1080/15230430.2022.2117765 ( reposiTUm)

Monti, G. S., & Filzmoser, P. (2022). A robust knockoff filter for sparse regression analysis of microbiome compositional data. Computational Statistics, 271–288. https://doi.org/10.1007/s00180-022-01268-7 ( reposiTUm)

Pfeiffer, P., Ronai, B., Vorlaufer, G., Dörr, N., & Filzmoser, P. (2022). Weighted LASSO variable selection for the analysis of FTIR spectra applied to the prediction of engine oil degradation. Chemometrics and Intelligent Laboratory Systems, 228, Article 104617. https://doi.org/10.1016/j.chemolab.2022.104617 ( reposiTUm)

Lubbe, S., Filzmoser, P., & Templ, M. (2021). Comparison of Zero Replacement Strategies for Compositional Data with Large Numbers of Zeros. Chemometrics and Intelligent Laboratory Systems, 210(104248), 104248. https://doi.org/10.1016/j.chemolab.2021.104248 ( reposiTUm)

Pérez-Fernández, S., Martínez-Camblor, P., Filzmoser, P., & Corral, N. (2021). Visualizing the decision rules behind the ROC curves: understanding the classification process. AStA Advances in Statistical Analysis, 105(1), 135–161. https://doi.org/10.1007/s10182-020-00385-2 ( reposiTUm)

Miksova, D., Rieser, C., Filzmoser, P., Middleton, M., & Sutinen, R. (2021). Identification of Mineralization in Geochemistry for Grid Sampling Using Generalized Additive Models. Mathematical Geosciences, 53(8), 1861–1880. https://doi.org/10.1007/s11004-021-09929-x ( reposiTUm)

Hron, K., Menafoglio, A., Palarea-Albaladejo, J., Filzmoser, P., Talská, R., & Egozcue, J. J. (2021). Weighting of Parts in Compositional Data Analysis: Advances and Applications. Mathematical Geosciences, 54(1), 71–93. https://doi.org/10.1007/s11004-021-09952-y ( reposiTUm)

Hron, K., Coenders, G., Filzmoser, P., & Palarea-Albaladejo, J. (2021). Analysing Pairwise Logratios Revisited. Mathematical Geosciences, 53(7), 1643–1666. https://doi.org/10.1007/s11004-021-09938-w ( reposiTUm)

Mikšová, D., Rieser, C., & Filzmoser, P. (2021). Identification of Mineralization in Geochemistry Along a Transect Based on the Spatial Curvature of Log-Ratios. Mathematical Geosciences, 53(7), 1513–1533. https://doi.org/10.1007/s11004-021-09930-4 ( reposiTUm)

Monti, G. S., & Filzmoser, P. (2021). Sparse least trimmed squares regression with compositional covariates for high-dimensional data. Bioinformatics, 37(21), 3805–3814. https://doi.org/10.1093/bioinformatics/btab572 ( reposiTUm)

Monti, G. S., & Filzmoser, P. (2021). Robust logistic zero-sum regression for microbiome compositional data. Advances in Data Analysis and Classification, 16(2), 301–324. https://doi.org/10.1007/s11634-021-00465-4 ( reposiTUm)

Moreau, L., & Filzmoser, P. (2021). Adaptive Trade-offs Towards the Last Glacial Maximum in North-Western Europe: a Multidisciplinary View from Walou Cave. Journal of Paleolithic Archaeology, 4, Article 11. https://doi.org/10.1007/s41982-021-00078-5 ( reposiTUm)

Mumic, N., & Filzmoser, P. (2021). A multivariate test for detecting fraud based on Benford’s law, with application to music streaming data. Statistical Methods and Applications, 30(3), 819–840. https://doi.org/10.1007/s10260-021-00582-6 ( reposiTUm)

Silva, A., Pedro,Duarte, Brito, P., Filzmoser, P., & Dias, J., G. (2021). MAINT.Data: Modelling and Analysing Interval Data in R. The R Journal, 13(2), 336–364. https://doi.org/10.32614/rj-2021-074 ( reposiTUm)

Drastichová, M., & Filzmoser, P. (2021). Factors of Quality of Life in a Group of Selected European Union and OECD Countries. Problemy Ekorozwoju, 16(2), 75–93. https://doi.org/10.35784/pe.2021.2.09 ( reposiTUm)

Rieser, C., & Filzmoser, P. (2021). Compositional trend filtering. Annales Mathematicae et Informaticae, 53, 257–270. https://doi.org/10.33039/ami.2021.02.004 ( reposiTUm)

Štefelová, N., Alfons, A., Palarea-Albaladejo, J., Filzmoser, P., & Hron, K. (2021). Robust regression with compositional covariates including cellwise outliers. Advances in Data Analysis and Classification, 15(4), 869–909. https://doi.org/10.1007/s11634-021-00436-9 ( reposiTUm)

Filzmoser, P., & Nordhausen, K. (2021). Robust linear regression for high-dimensional data: an overview. Wiley Interdisciplinary Reviews: Computational Statistics. https://doi.org/10.1002/wics.1524 ( reposiTUm)

de Sousa, J., Hron, K., Fačevicová, K., & Filzmoser, P. (2021). Robust principal component analysis for compositional tables. Journal of Applied Statistics, 48(2), 214–233. https://doi.org/10.1080/02664763.2020.1722078 ( reposiTUm)

Nordhausen, K., Fischer, G., & Filzmoser, P. (2021). Blind Source Separation for Compositional Time Series. Mathematical Geosciences, 53(5), 905–924. https://doi.org/10.1007/s11004-020-09869-y ( reposiTUm)

de la Rosa de Saa, S., Lubiano, M. A., Sinova, B., Gil, M. Á., & Filzmoser, P. (2021). Location-free robust scale estimates for fuzzy data. IEEE Transactions on Fuzzy Systems, 29(6), 1682–1694. https://doi.org/10.1109/tfuzz.2020.2984203 ( reposiTUm)

Varmuza, K., Dehmer, M., Emmert-Streib, F., & Filzmoser, P. (2021). Automorphism groups of alkane graphs. Croatica Chemica Acta, 94(1). https://doi.org/10.5562/cca3807 ( reposiTUm)

Ortner, T., Filzmoser, P., Rohm, M., Brodinova, S., & Breiteneder, C. (2021). Local projections for high-dimensional outlier detection. Metron, 79(2), 189–206. https://doi.org/10.1007/s40300-020-00183-5 ( reposiTUm)

Rabeder, J., Reitner, H., Wimmer-Frey, I., Filzmoser, P., Mert, M. C., Heinrich, M., Lipiarski, P., Reitner, J. M., Hobinger, G., & Benold, C. (2021). Integrative Analyse der L oss- und L osslehmvorkommen im osterreichischen Alpenvorland und im Wiener Becken { ein Beitrag zum Interaktiven Rohsto -Informationssystem IRIS-Online. BHM Berg- Und Hüttenmännische Monatshefte, 166(4), 206–211. https://doi.org/10.1007/s00501-021-01096-0 ( reposiTUm)

Rosadi, D., Setiawan, E. P., Templ, M., & Filzmoser, P. (2020). Robust Covariance Estimators for Mean-Variance Portfolio Optimization with Transaction Lots. Operations Research Perspectives, 7, Article 100154. https://doi.org/10.1016/j.orp.2020.100154 ( reposiTUm)

Ortner, I., Filzmoser, P., & Croux, C. (2020). Robust and sparse multigroup classification by the optimal scoring approach. Data Mining and Knowledge Discovery, 34(3), 723–741. https://doi.org/10.1007/s10618-019-00666-8 ( reposiTUm)

Mikšová, D., Filzmoser, P., & Middleton, M. (2020). Imputation of values above an upper detection limit in compositional data. Computers and Geosciences, 136, Article 104383. https://doi.org/10.1016/j.cageo.2019.104383 ( reposiTUm)

Varmuza, K., Filzmoser, P., Fray, N., Cottin, H., Merouane, S., Stenzel, O., Paquette, J., Kissel, J., Briois, C., Baklouti, D., Bardyn, A., Siljeström, S., Silén, J., & Hilchenbach, M. (2020). Composition of cometary particles collected during two periods of the Rosetta mission: multivariate evaluation of mass spectral data. Journal of Chemometrics, 34(4). https://doi.org/10.1002/cem.3218 ( reposiTUm)

Filzmoser, P., & Gregorich, M. (2020). Multivariate outlier detection in applied data analysis: global, local, compositional and cellwise outliers. Mathematical Geosciences, 52(8), 1049–1066. https://doi.org/10.1007/s11004-020-09861-6 ( reposiTUm)

van den Boogaart, K. G., Filzmoser, P., Hron, K., Templ, M., & Tolosana-Delgado, R. (2020). Classical and Robust Regression Analysis with Compositional Data. Mathematical Geosciences, 53(5), 823–858. https://doi.org/10.1007/s11004-020-09895-w ( reposiTUm)

Lemière, B., Melleton, J., Auger, P., Derycke, V., Gloaguen, E., Bouat, L., Mikšová, D., Filzmoser, P., & Middleton, M. (2020). pXRF measurements on soil samples for the exploration of an antimony deposit: example from the Vendean antimony district (France). Minerals, 10(8), Article 724. https://doi.org/10.3390/min10080724 ( reposiTUm)

Acitas, S., Filzmoser, P., & Senoglu, B. (2020). A robust adaptive modified maximum likelihood estimator for the linear regression model. Journal of Statistical Computation and Simulation, 91(7), 1394–1414. https://doi.org/10.1080/00949655.2020.1856847 ( reposiTUm)

Vencálek, O., Hron, K., & Filzmoser, P. (2020). A comparison of generalised linear models and compositional models for ordered categorical data. Statistical Modelling, 20(3), 249–273. https://doi.org/10.1177/1471082x18816540 ( reposiTUm)

Filzmoser, P., Höppner, S., Ortner, I., Serneels, S., & Verdonck, T. (2020). Cellwise robust M regression. Computational Statistics & Data Analysis, 147, Article 106944. https://doi.org/10.1016/j.csda.2020.106944 ( reposiTUm)

Drastichova, M., & Filzmoser, P. (2020). The relationship between health outcomes and health expenditure in Europe by using compositional data analysis. Problemy Ekorozwoju, 15(2), 99–110. http://hdl.handle.net/20.500.12708/140712 ( reposiTUm)

Mikšová, D., Rieser, C., Filzmoser, P., Thaarup, S. M., & Melleton, J. (2020). A method to identify geochemical mineralization on linear transects. Austrian Journal of Statistics, 49(4), 89–98. https://doi.org/10.17713/ajs.v49i4.1133 ( reposiTUm)

Hron, K., Engle, M., Filzmoser, P., & Fišerová, E. (2020). Weighted symmetric pivot coordinates for compositional data with geochemical applications. Mathematical Geosciences, 53(4), 655–674. https://doi.org/10.1007/s11004-020-09862-5 ( reposiTUm)

Acitas, S., Filzmoser, P., & Senoglu, B. (2020). A new partial robust adaptive modified maximum likelihood estimator. Chemometrics and Intelligent Laboratory Systems, 204, Article 104068. https://doi.org/10.1016/j.chemolab.2020.104068 ( reposiTUm)

Templ, M., Gussenbauer, J., & Filzmoser, P. (2020). Evaluation of robust outlier detection methods for zero-inflated complex data. Journal of Applied Statistics, 47(7), 1144–1167. https://doi.org/10.1080/02664763.2019.1671961 ( reposiTUm)

Drastichova, M., & Filzmoser, P. (2019). Assessment of sustainable development using cluster analysis and principal component analysis. Problemy Ekorozwoju, 14(2), 7–24. http://hdl.handle.net/20.500.12708/142771 ( reposiTUm)

Walach, J., Filzmoser, P., Kouřil, Š., Friedecký, D., & Adam, T. (2019). Cellwise outlier detection and biomarker identification in metabolomics based on pairwise log ratios. Journal of Chemometrics, 34(1), Article e3182. https://doi.org/10.1002/cem.3182 ( reposiTUm)

Brodinová, Š., Filzmoser, P., Ortner, T., Breiteneder, C., & Rohm, M. (2019). Robust and sparse k-means clustering for high-dimensional data. Advances in Data Analysis and Classification, 905–932. https://doi.org/10.1007/s11634-019-00356-9 ( reposiTUm)

Moreau, L., Ciornei, A., Gjesfjeld, E., Filzmoser, P., Gibson, S. A., Day, J., Nigst, P. R., Noiret, P., Macleod, R. A., Niţă, L., & Anghelinu, M. (2019). First geochemical “fingerprinting” of Balkan and Prut flint from Palaeolithic Romania: potentials, limitations and future directions. Archaeometry, 61(3), 521–538. https://doi.org/10.1111/arcm.12433 ( reposiTUm)

Filzmoser, P., & Hron, K. (2019). Comments on: Composition data: the sample space and its structure. TEST, 28(3), 639–643. https://doi.org/10.1007/s11749-019-00671-5 ( reposiTUm)

Rosadi, D., & Filzmoser, P. (2019). Robust second-order least-squares estimation for regression models with autoregressive errors. Statistical Papers, 60(1), 105–122. https://doi.org/10.1007/s00362-016-0829-9 ( reposiTUm)

Abreu-Junior, C. H., de Lima Brossi, M. J., Monteiro, R. T., Cardoso, P. H. S., da Silva Mandu, T., Nogueira, T. A. R., Ganga, A., Filzmoser, P., Carvalho de Oliveira, F., Pittol Firme, L., He, Z., & Capra, G. F. (2019). Effects of sewage sludge application on unfertile tropical soils evaluated by multiple approaches: a field experiment in a commercial Eucalyptus plantation. Science of the Total Environment, 655, 1457–1467. https://doi.org/10.1016/j.scitotenv.2018.11.334 ( reposiTUm)

Templ, M., Gussenbauer, J., & Filzmoser, P. (2019). Evaluation of robust outlier detection methods for zero-inflated complex data. Journal of Applied Statistics, 47(7), 1144–1167. https://doi.org/10.1080/02664763.2019.1671961 ( reposiTUm)

Gozzi, C., Filzmoser, P., Buccianti, A., Vaselli, O., & Nisi, B. (2019). Statistical methods for the geochemical characterisation of surface waters: The case study of the Tiber River basin (Central Italy). Computers and Geosciences, 131, 80–88. https://doi.org/10.1016/j.cageo.2019.06.011 ( reposiTUm)

Brodinova, S., Zaharieva, M., Filzmoser, P., Ortner, T., & Breiteneder, C. (2018). Clustering of imbalanced high-dimensional media data. Advances in Data Analysis and Classification, 261–284. https://doi.org/10.1007/s11634-017-0292-z ( reposiTUm)

Flem, B., Reimann, C., Fabian, K., Birke, M., Filzmoser, P., & Banks, D. (2018). Graphical statistics to explore the natural and anthropogenic processes influencing the inorganic quality of drinking water, ground water and surface water. Applied Geochemistry, 88, 133–148. https://doi.org/10.1016/j.apgeochem.2017.09.006 ( reposiTUm)

Duarte Silva, A. P., Filzmoser, P., & Brito, P. (2018). Outlier detection in interval data. Advances in Data Analysis and Classification, 12(3), 785–822. https://doi.org/10.1007/s11634-017-0305-y ( reposiTUm)

Kurnaz, F. S., Hoffmann, I., & Filzmoser, P. (2018). Robust and sparse estimation methods for high dimensional linear and logistic regression. Chemometrics and Intelligent Laboratory Systems, 172, 211–222. https://doi.org/10.1016/j.chemolab.2017.11.017 ( reposiTUm)

Capra, G. F., Tidu, S., Lovreglio, R., Certini, G., Salis, M., Bacciu, V., Ganga, A., & Filzmoser, P. (2018). The impact of large fire on calcareous Mediterranean pedosystems (Sardinia, Italy) - An integrated multiple approach. Science of the Total Environment, 624, 1152–1162. https://doi.org/10.1016/j.scitotenv.2017.12.099 ( reposiTUm)

Ortner, T., Filzmoser, P., Rohm, M., Breiteneder, C., & Brodinova, S. (2018). Guided projections for analyzing the structure of high-dimensional data. Journal of Computational and Graphical Statistics, 27(4), 750–762. https://doi.org/10.1080/10618600.2018.1459304 ( reposiTUm)

Monti, G. S., Filzmoser, P., & Deutsch, R. C. (2018). A robust approach to risk assessment based on species sensitivity distributions. Risk Analysis, 38(10), 2073–2086. https://doi.org/10.1111/risa.13009 ( reposiTUm)

Reimann, C., Englmaier, P., Flem, B., Eggen, O. A., Finne, T. E., Andersson, M., & Filzmoser, P. (2018). The response of 12 different plant materials and one mushroom to Mo and Pb mineralization along a 100-km transect in southern central Norway. Geochemistry: Exploration, Environment, Analysis, 18(3), 204–215. https://doi.org/10.1144/geochem2017-089 ( reposiTUm)

Zimek, A., & Filzmoser, P. (2018). There and back again: Outlier detection between statistical reasoning and data mining algorithms. WIREs Data Mining and Knowledge Discovery, 8(6). https://doi.org/10.1002/widm.1280 ( reposiTUm)

Landauer, M., Wurzenberger, M., Skopik, F., Settanni, G., & Filzmoser, P. (2018). Dynamic log file analysis: An unsupervised cluster evolution approach for anomaly detection. Computers and Security, 79, 94–116. https://doi.org/10.1016/j.cose.2018.08.009 ( reposiTUm)

Pérez-Fernández, S., Martínez-Camblor, P., Filzmoser, P., & Corral, N. (2018). nsROC: An R package for Non-Standard ROC Curve Analysis. The R Journal, 10(2), 55. https://doi.org/10.32614/rj-2018-043 ( reposiTUm)

Brandl, M., Martinez, M. M., Hauzenberger, C., Filzmoser, P., Nymoen, P., & Mehler, N. (2018). A multi-technique analytical approach to sourcing Scandinavian flint: Provenance of ballast flint from the shipwreck “Leirvigen 1”, Norway. PLoS ONE, 13(8), e0200647. https://doi.org/10.1371/journal.pone.0200647 ( reposiTUm)

Reimann, C., Fabian, K., Flem, B., Anderson, M., Filzmoser, P., & Englmaier, P. (2018). Geosphere-biosphere circulation of chemical elements in soil and plant systems from a 100 km transect from southern central Norway. Science of the Total Environment, 639, 129–145. https://doi.org/10.1016/j.scitotenv.2018.05.070 ( reposiTUm)

Mert, M. C., Filzmoser, P., Endel, G., & Wilbacher, I. (2018). Compositional data analysis in epidemiology. Statistical Methods in Medical Research, 27(6), 1878–1891. https://doi.org/10.1177/0962280216671536 ( reposiTUm)

Filzmoser, P., & Kurnaz, F. S. (2018). A robust Liu regression estimator. Communications in Statistics - Simulation and Computation, 47(2), 432–443. https://doi.org/10.1080/03610918.2016.1271889 ( reposiTUm)

Reimann, C., Fabian, K., Birke, M., Filzmoser, P., Demetriades, A., Négrel, P., Oorts, K., Matschullat, J., & de Caritat, P. (2018). GEMAS: Establishing geochemical back- ground and threshold for 53 chemical elements in European agricultural soil. Applied Geochemistry, 88, 302–318. https://doi.org/10.1016/j.apgeochem.2017.01.021 ( reposiTUm)

Di Palma, M. A., Filzmoser, P., Gallo, M., & Hron, K. (2018). A robust Parafact model for compositional data. Journal of Applied Statistics, 45(8), 1347–1369. https://doi.org/10.1080/02664763.2017.1381669 ( reposiTUm)

Varmuza, K., Filzmoser, P., Hoffmann, I., Walach, J., Cottin, H., Fray, N., BRIOIS, C., Modica, P., Bardyn, A., Silén, J., Siljeström, S., Stenzel, O., Kissel, J., & Hilchenbach, M. (2018). Significance of variables for discrimination: Applied to the search of organic ions in mass spectra measured on cometary particles. Journal of Chemometrics, 32(4), e3001. https://doi.org/10.1002/cem.3001 ( reposiTUm)

Bögl, M., Filzmoser, P., Gschwandtner, T., Lammarsch, T., Leite, R. A., Miksch, S., & Rind, A. (2017). Cycle Plot Revisited: Multivariate Outlier Detection Using a Distance-Based Abstraction. Computer Graphics Forum, 36(3), 227–238. http://hdl.handle.net/20.500.12708/146628 ( reposiTUm)

Walach, J., Filzmoser, P., Hron, K., Walczak, B., & Najdekr, L. (2017). Robust biomarker identification in a two-class problem based on pairwise log-ratios. Chemometrics and Intelligent Laboratory Systems, 171, 277–285. https://doi.org/10.1016/j.chemolab.2017.09.003 ( reposiTUm)

Templ, B., Templ, M., Filzmoser, P., Lehoczky, A., Baksiene, E., Fleck, S., Gregow, H., Hodzic, S., Kalvane, G., Kubin, E., Palm, V., Romanovskaja, D., Vucetic, V., Zust, A., & Czúcz, B. (2017). Phenological patterns of flowering across biogeographical regions of Europe. International Journal of Biometeorology, 61(7), 1347–1358. https://doi.org/10.1007/s00484-017-1312-6 ( reposiTUm)

Stenzel, O., Hilchenbach, M., Merouane, S., Paquette, J., Varmuza, K., Engrand, C., Brandstätter, F., Koeberl, C., Ferrière, L., Filzmoser, P., & Siljeström, S. (2017). Similarities in element content between comet 67P/Churyumov–Gerasimenko coma dust and selected meteorite samples. Monthly Notices of the Royal Astronomical Society, 469(Suppl_2), S492–S505. https://doi.org/10.1093/mnras/stx1908 ( reposiTUm)

Filzmoser, P., & Kharin, Y. (2017). Special issue of the CDAM 2016 conference. Austrian Journal of Statistics, 46(3–4), 1–2. http://hdl.handle.net/20.500.12708/146925 ( reposiTUm)

Hron, K., Filzmoser, P., de Caritat, P., Fišerová, E., & Gardlo, A. (2017). Weighted Pivot Coordinates for Compositional Data and Their Application to Geochemical Mapping. Mathematical Geosciences, 49(6), 797–814. https://doi.org/10.1007/s11004-017-9684-z ( reposiTUm)

Tobin, J., Walach, J., de Beer, D., Williams, P. J., Filzmoser, P., & Walczak, B. (2017). Untargeted analysis of chromatographic data for green and fermented rooibos: problem with size effect removal. Journal of Chromatography A, 1525, 109–115. https://doi.org/10.1016/j.chroma.2017.10.024 ( reposiTUm)

Reimann, C., Filzmoser, P., Hron, K., Kynčlová, P., & Garrett, R. G. (2017). A new method for correlation analysis of compositional (environmental) data - a worked example. Science of the Total Environment, 607–608, 965–971. https://doi.org/10.1016/j.scitotenv.2017.06.063 ( reposiTUm)

Templ, M., Hron, K., & Filzmoser, P. (2017). Exploratory Tools for Outlier Detection in Compositional Data with Structural Zeros. Journal of Applied Statistics, 44(4), 734–752. https://doi.org/10.1080/02664763.2016.1182135 ( reposiTUm)

Hron, K., Brito, P., & Filzmoser, P. (2017). Exploratory data analysis for interval compositional data. Advances in Data Analysis and Classification, 11(2), 223–241. https://doi.org/10.1007/s11634-016-0245-y ( reposiTUm)

Alfons, A., Croux, C., & Filzmoser, P. (2017). Robust Maximum Association Estimators. Journal of the American Statistical Association, 112(517), 436–445. https://doi.org/10.1080/01621459.2016.1148609 ( reposiTUm)

Reimann, C., Négrel, P., Ladenberger, A., Birke, M., Filzmoser, P., O’Connor, P., & Demetriades, A. (2017). Comment on “Maps of heavy metals in the soils of the European Union and proposed priority areas for detailed assessment” by Tóth, G., Hermann, T., Szatmári, G., Pásztor, L. Science of the Total Environment, 578, 236–241. https://doi.org/10.1016/j.scitotenv.2016.07.208 ( reposiTUm)

Glock, B., Endel, F., Endel, G., Sandholzer, K., Popper, N., Rinner, C., Duftschmid, G., Filzmoser, P., Mert, M. C., Holl, J., & Wagner-Pinter, M. (2017). How sick is Austria? - A decision support framework for different evaluations of the burden of disease within the Austrian population based on different data sources. International Journal of Population Data Science, 1(1). https://doi.org/10.23889/ijpds.v1i1.111 ( reposiTUm)

de la Rosa de Sáa, S., Lubiano, M. A., Sinova, B., & Filzmoser, P. (2017). Robust scale estimators for fuzzy data. Advances in Data Analysis and Classification, 11(4), 731–758. https://doi.org/10.1007/s11634-015-0210-1 ( reposiTUm)

Kynčlová, P., Hron, K., & Filzmoser, P. (2017). Correlation Between Compositional Parts Based on Symmetric Balances. Mathematical Geosciences, 49(6), 777–796. https://doi.org/10.1007/s11004-016-9669-3 ( reposiTUm)

Alfons, A., Croux, C., & Filzmoser, P. (2016). Robust maximum association between data sets: The R package ccaPP. Austrian Journal of Statistics, 45(1), 71–79. https://doi.org/10.17713/ajs.v45i1.90 ( reposiTUm)

Capra, G. F., Ganga, A., Filzmoser, P., Gaviano, C., & Vacca, S. (2016). Combining local and scientific knowledge on soil resources through an integrated ethnopedological approach. CATENA, 142, 89–101. https://doi.org/10.1016/j.catena.2016.03.003 ( reposiTUm)

Hrůzová, K., Todorov, V., Hron, K., & Filzmoser, P. (2016). Classical and robust orthogonal regression between parts of compositional data. Statistics, 50(6), 1261–1275. https://doi.org/10.1080/02331888.2016.1162164 ( reposiTUm)

Moreau, L., Brandl, M., Filzmoser, P., Hauzenberger, C., Goemaere, E., Jadin, I., Collet, H., Hanzeur, A., & Schmitz, R. (2016). Geochemical Sourcing of Flint Artifacts from Western Belgium and the German Rhineland: Testing Hypotheses on Gravettian Period Mobility and Raw Material Economy. Geoarchaeology, 31(3), 229–243. http://hdl.handle.net/20.500.12708/149146 ( reposiTUm)

McKinley, J. M., Hron, K., Grunsky, E. C., Reimann, C., de Caritat, P., Filzmoser, P., van den Boogaart, K. G., & Tolosana-Delgado, R. (2016). The single component geochemical map: Fact or fiction? Journal of Geochemical Exploration, 162, 16–28. https://doi.org/10.1016/j.gexplo.2015.12.005 ( reposiTUm)

Filzmoser, P., Hron, K., & Tolosana-Delgado, R. (2016). Statistical analysis of geochemical compositions: Problems, perspectives and solutions. Applied Geochemistry, 75, 169–170. https://doi.org/10.1016/j.apgeochem.2016.11.016 ( reposiTUm)

Kynčlová, P., Filzmoser, P., & Hron, K. (2016). Compositional biplots including external non-compositional variables. Statistics, 50(5), 1132–1148. https://doi.org/10.1080/02331888.2015.1135155 ( reposiTUm)

Hoffmann, I., Filzmoser, P., Serneels, S., & Varmuza, K. (2016). Sparse and robust PLS for binary classification. Journal of Chemometrics, 30(4), 153–162. https://doi.org/10.1002/cem.2775 ( reposiTUm)

Templ, M., Hron, K., Filzmoser, P., & Gardlo, A. (2016). Imputation of rounded zeros for high-dimensional compositional data. Chemometrics and Intelligent Laboratory Systems, 155, 183–190. https://doi.org/10.1016/j.chemolab.2016.04.011 ( reposiTUm)

Reimann, C., Négrel, P., Ladenberger, A., Birke, M., Filzmoser, P., O’Connor, P., & Demetriades, A. (2016). Comment on “Heavy metals in agricultural soil of the European Union with implications for food safety” by G. Toth, T. Hermann, M.R. Da Silva, and L. Montanarella. Environment International, 97, 258–263. https://doi.org/10.1016/j.envint.2016.07.019 ( reposiTUm)

Mert, M. C., Filzmoser, P., & Hron, K. (2016). Error propagation in isometric log-ratio coordinates for compositional data: theoretical and practical considerations. Mathematical Geosciences. https://doi.org/10.1007/s11004-016-9646-x ( reposiTUm)

Mert, M. C., Filzmoser, P., & Hron, K. (2015). Sparse principal balances. Statistical Modelling, 15(2), 159–174. https://doi.org/10.1177/1471082x14535525 ( reposiTUm)

Hron, K., Menafoglio, A., Templ, M., Hruzova, K., & Filzmoser, P. (2015). Simplicial principal component analysis for density functions in Bayes spaces. Computational Statistics & Data Analysis, 94, 330–350. https://doi.org/10.1016/j.csda.2015.07.007 ( reposiTUm)

Flem, B., Reimann, C., Birke, M., Banks, D., Filzmoser, P., & Frengstad, B. (2015). Inorganic chemical quality of European tap-water: 2. Geographical distribution. Applied Geochemistry, 59, 211–224. https://doi.org/10.1016/j.apgeochem.2015.01.016 ( reposiTUm)

Kynčlová, P., Filzmoser, P., & Hron, K. (2015). Modeling Compositional Time Series with Vector Autoregressive Models. Journal of Forecasting, 34(4 / July), 303–314. http://hdl.handle.net/20.500.12708/150883 ( reposiTUm)

Martin-Fernandez, J. A., Hron, K., Templ, M., Filzmoser, P., & Palarea-Albaladejo, J. (2015). Bayesian-multiplicative treatment of count zeros in compositional data sets. Statistical Modelling, 15(2), 134–158. http://hdl.handle.net/20.500.12708/150426 ( reposiTUm)

Nordhausen, K., Oja, H., Filzmoser, P., & Reimann, C. (2015). Blind Source Separation for Spatial Compositional Data. Mathematical Geosciences, 47(7), 753–770. https://doi.org/10.1007/s11004-014-9559-5 ( reposiTUm)

Ortner, T., Filzmoser, P., & Endel, G. (2015). Identifying Structural Changes in Austrian Social Insurance Data. IFAC-PapersOnLine, 48(1), 115–120. https://doi.org/10.1016/j.ifacol.2015.05.152 ( reposiTUm)

Kalivodova, A., Hron, K., Filzmoser, P., Najdekr, L., Janeckova, H., & Adam, T. (2015). PLS-DA for compositional data with application to metabolomics. Journal of Chemometrics, 29(1), 21–28. http://hdl.handle.net/20.500.12708/151633 ( reposiTUm)

Hoffmann, I., Serneels, S., Filzmoser, P., & Croux, C. (2015). Sparse partial robust M regression. Chemometrics and Intelligent Laboratory Systems, 149, PART A, 15 DECEMBER, 50–59. http://hdl.handle.net/20.500.12708/151632 ( reposiTUm)

Filzmoser, P., & Hron, K. (2015). Guest Editorial: Special Issue: Compositional Data Modelling. Statistical Modelling, 15(2), vii–viii. https://doi.org/10.1177/1471082x14535520 ( reposiTUm)

Todorov, V., & Filzmoser, P. (2014). Software tools for robust analysis of high-dimensional data. Austrian Journal of Statistics, 43(4), 255–266. https://doi.org/10.17713/ajs.v43i4.44 ( reposiTUm)

Kalcher, K., Boubela, R. N., Huf, W., Bartova, L., Kronnerwetter, C., Derntl, B., Pezawas, L., Filzmoser, P., Nasel, C., & Moser, E. (2014). The Spectral Diversity of Resting-State Fluctuations in the Human Brain. PLoS ONE, 9(4), e93375. https://doi.org/10.1371/journal.pone.0093375 ( reposiTUm)

Huf, W., Kalcher, K., Boubela, R. N., Rath, G., Vecsei, A., Filzmoser, P., & Moser, E. (2014). On the generalizability of resting-state fMRI machine learning classifiers. Frontiers in Human Neuroscience, 8. https://doi.org/10.3389/fnhum.2014.00502 ( reposiTUm)

Brandl, M., Hauzenberger, C., Postl, W., Martinez, M. M., Filzmoser, P., & Trnaka, G. (2014). Radiolarite studies at Krems-Wachtberg (Lower Austria): Northern Alpine versus Carpathian lithic resources. Quaternary International, 351, 146–162. http://hdl.handle.net/20.500.12708/157439 ( reposiTUm)

Nordhausen, K., Oja, H., Filzmoser, P., & Reimann, C. (2014). Blind source separation for spatial compositional data. Mathematical Geosciences, 47(7), 753–770. https://doi.org/10.1007/s11004-014-9559-5 ( reposiTUm)

Filzmoser, P., Ruiz-Gazen, A., & Thomas-Agnan, C. (2014). Identification of local multivariate outliers. Statistical Papers, 55(1), 29–47. https://doi.org/10.1007/s00362-013-0524-z ( reposiTUm)

Neykov, N. M., Filzmoser, P., & Neytchev, P. N. (2014). Ultrahigh dimensional variable selection through the penalized maximum trimmed likelihood estimator. Statistical Papers, 55(1), 187–207. https://doi.org/10.1007/s00362-013-0516-z ( reposiTUm)

Varmuza, K., Filzmoser, P., Hilchenbach, M., Krüger, H., & Silén, J. (2014). KNN classification - evaluated by repeated double cross validation: Recognition of minerals relevant for comet dust. Chemometrics and Intelligent Laboratory Systems, 138, 64–71. https://doi.org/10.1016/j.chemolab.2014.07.011 ( reposiTUm)

Filzmoser, P., & Walczak, B. (2014). What can go wrong at the data normalization step for identification of biomarkers? Journal of Chromatography A, 1362, 194–205. https://doi.org/10.1016/j.chroma.2014.08.050 ( reposiTUm)

Filzmoser, P., Gatu, C., & Zeileis, A. (2014). Special issue on statistical algorithms and software in R. Computational Statistics & Data Analysis, 71, 887–888. https://doi.org/10.1016/j.csda.2013.10.012 ( reposiTUm)

Templ, M., & Filzmoser, P. (2014). Simulation and quality of a synthetic close-to-reality employer-employee population. Journal of Applied Statistics, 41(5), 1053–1072. https://doi.org/10.1080/02664763.2013.859237 ( reposiTUm)

Templ, M., Aklan, S., Filzmoser, P., Preusser, M., & Hainfellner, J. A. (2013). Statistical Indicators for the Analysis of Digitalized Brain Tumor Images. Austrian Journal of Statistics, 42(2), 1–19. https://doi.org/10.17713/ajs.v42i2.156 ( reposiTUm)

Bögl, M., Aigner, W., Filzmoser, P., Lammarsch, T., Miksch, S., & Rind, A. (2013). Visual Analytics for Model Selection in Time Series Analysis. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2237–2246. https://doi.org/10.1109/tvcg.2013.222 ( reposiTUm)

Boubela, R. N., Kalcher, K., Huf, W., Kronnerwetter, C., Filzmoser, P., & Moser, E. (2013). Beyond noise: Using temporal ICA to extract meaningful information from high-frequency fMRI signal fluctuations during rest. Frontiers in Human Neuroscience, 7. https://doi.org/10.3389/fnhum.2013.00168 ( reposiTUm)

Martinez Avila, J. C., Filzmoser, P., & Neykov, N. (2013). Statistical modeling of hunting success using hunter surveys. Austrian Journal of Statistics, 42(2), 67–80. http://hdl.handle.net/20.500.12708/155977 ( reposiTUm)

Alfons, A., Templ, M., & Filzmoser, P. (2013). Robust estimation of economic indicators from survey samples based on Pareto tail modelling. Journal of the Royal Statistical Society: Series C, VOL. 62(2), 271–286. http://hdl.handle.net/20.500.12708/155672 ( reposiTUm)

Todeschini, R., Ballabio, D., Consonni, V., Sahigara, F., & Filzmoser, P. (2013). Locally-centred Mahalanobis distance: a new distance measure with salient features towards outlier detection. Analytica Chimica Acta, 787, 1–9. https://doi.org/10.1016/j.aca.2013.04.034 ( reposiTUm)

Croux, C., Filzmoser, P., & Fritz, H. (2013). Robust Sparse Principal Component Analysis. Technometrics, 55(2), 202–214. https://doi.org/10.1080/00401706.2012.727746 ( reposiTUm)

Hron, K., Filzmoser, P., Donevska, S., & Fišerová, E. (2013). Covariance-based variable selection for compositional data. Mathematical Geosciences, 45(4), 487–498. https://doi.org/10.1007/s11004-013-9450-9 ( reposiTUm)

Ballabio, D., Vasighi, M., & Filzmoser, P. (2013). Effects of supervised Self Organising Maps parameters on classification performance. Analytica Chimica Acta, 765, 45–53. https://doi.org/10.1016/j.aca.2012.12.027 ( reposiTUm)

Hron, K., Templ, M., & Filzmoser, P. (2013). Estimation of a proportion in survey sampling using the logratio approach. Metrika, 76(6), 799–818. https://doi.org/10.1007/s00184-012-0416-6 ( reposiTUm)

Varmuza, K., Filzmoser, P., & Dehmer, M. (2013). Multivariate linear QSPR/QSAR models: Rigorous evaluation of variable selection for PLS. Computational and Structural Biotechnology Journal, 5(e201302007), e201302007. https://doi.org/10.5936/csbj.201302007 ( reposiTUm)

Kalcher, K., Boubela, R., Huf, W., Biswal, B., Baldinger, P., Sailer, U., Filzmoser, P., Kasper, S., Lamm, C., Lanzenberger, R., & Moser, E. (2013). RESCALE: Voxel-specific Task-fMRI Scaling Using Resting State Fluctuation Amplitude. NeuroImage, 70(15 April), 80–88. http://hdl.handle.net/20.500.12708/155770 ( reposiTUm)

Reimann, C., Filzmoser, P., Fabian, K., Hron, K., Birke, M., Demetriades, A., Dinelli, E., & Ladenberger, A. (2012). The concept of compositional data analysis in practice - Total major element concentrations in agricultural and grazing land soils of Europe. Science of the Total Environment, 426, 196–210. https://doi.org/10.1016/j.scitotenv.2012.02.032 ( reposiTUm)

Reimann, C., Birke, M., & Filzmoser, P. (2012). Temperature-dependent leaching of chemical elements from mineral water bottle materials. Applied Geochemistry, 27(8), 1492–1498. https://doi.org/10.1016/j.apgeochem.2012.05.003 ( reposiTUm)

Filzmoser, P., Hron, K., & Templ, M. (2012). Discriminant analysis for compositional data and robust parameter estimation. Computational Statistics, 27(4), 585–604. https://doi.org/10.1007/s00180-011-0279-8 ( reposiTUm)

Fritz, H., Filzmoser, P., & Croux, C. (2012). A comparison of algorithms for the multivariate L1-median. Computational Statistics, 27(3), 393–410. https://doi.org/10.1007/s00180-011-0262-4 ( reposiTUm)

Filzmoser, P., Hron, K., & Reimann, C. (2012). Interpretation of multivariate outliers for compositional data. Computers and Geosciences, 39, 77–85. https://doi.org/10.1016/j.cageo.2011.06.014 ( reposiTUm)

Kalcher, K., Huf, W., Boubela, R. N., Filzmoser, P., Pezawas, L., Biswal, B., Kasper, S., Moser, E., & Windischberger, C. (2012). Fully exploratory network independent component analysis of the 1000 functional connectomes database. Frontiers in Human Neuroscience, 6. https://doi.org/10.3389/fnhum.2012.00301 ( reposiTUm)

Hron, K., Filzmoser, P., & Thompson, K. (2012). Linear regression with compositional explanatory variables. Journal of Applied Statistics, 39(5), 1115–1128. https://doi.org/10.1080/02664763.2011.644268 ( reposiTUm)

Filzmoser, P., & Todorov, V. (2012). Robust tools for the imperfect world. Information Sciences, 245, 4–20. https://doi.org/10.1016/j.ins.2012.10.017 ( reposiTUm)

Martín-Fernández, J. A., Hron, K., Templ, M., Filzmoser, P., & Palarea-Albaladejo, J. (2012). Model-based replacement of rounded zeros in compositional data: Classical and robust approaches. Computational Statistics & Data Analysis, 56(9), 2688–2704. https://doi.org/10.1016/j.csda.2012.02.012 ( reposiTUm)

Filzmoser, P., Gschwandtner, M., & Todorov, V. (2012). Review of sparse methods in regression and classification with application to chemometrics. Journal of Chemometrics, 26, 10. http://hdl.handle.net/20.500.12708/163566 ( reposiTUm)

Neykov, N. M., Filzmoser, P., & Neytchev, P. N. (2012). Robust joint modeling of mean and dispersion through trimming. Computational Statistics & Data Analysis, 56(1), 34–48. https://doi.org/10.1016/j.csda.2011.07.007 ( reposiTUm)

Sucharovà, J., Suchara, I., Hola, M., Marikova, S., Reimann, C., Boyd, R., Filzmoser, P., & Englmaier, P. (2012). Top-/bottom-soil ratios and enrichment factors: What do they really show? Applied Geochemistry, 27(1), 138–145. https://doi.org/10.1016/j.apgeochem.2011.09.025 ( reposiTUm)

Hron, K., Jelínková, M., Filzmoser, P., Kreuziger, R., Bednář, P., & Barták, P. (2012). Statistical analysis of wines using a robust compositional biplot. Talanta, 90, 46–50. https://doi.org/10.1016/j.talanta.2011.12.060 ( reposiTUm)

Egozcue, J. J., Daunis-I-Estadella, J., Pawlowsky-Glahn, V., Hron, K., & Filzmoser, P. (2012). Simplicial regression. The normal model. Journal of Applied Probability and Statistics, 6, 87–108. http://hdl.handle.net/20.500.12708/163520 ( reposiTUm)

Templ, M., Alfons, A., & Filzmoser, P. (2012). Exploring incomplete data using visualization techniques. Advances in Data Analysis and Classification, 6(1), 29–47. https://doi.org/10.1007/s11634-011-0102-y ( reposiTUm)

Neykov, N. M., Čížek, P., Filzmoser, P., & Neytchev, P. N. (2012). The least trimmed quantile regression. Computational Statistics & Data Analysis, 56(6), 1757–1770. https://doi.org/10.1016/j.csda.2011.10.023 ( reposiTUm)

Filzmoser, P., & Hron, K. (2012). Correlation Analysis for Compositional Data. Mathematical Geosciences, 41(8), 905–919. https://doi.org/10.1007/s11004-008-9196-y ( reposiTUm)

Varmuza, K., Filzmoser, P., Liebmann, B., & Dehmer, M. (2012). Redundancy analysis for characterizing the correlation between groups of variables - Applied to molecular descriptors. Chemometrics and Intelligent Laboratory Systems, 117, 31–41. https://doi.org/10.1016/j.chemolab.2011.05.013 ( reposiTUm)

Boubela, R. N., Huf, W., Kalcher, K., Sladky, R., Filzmoser, P., Pezawas, L., Kasper, S., Windischberger, C., & Moser, E. (2012). A highly parallelized framework for computationally intensive MR data analysis. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE, 25(4), 313–320. https://doi.org/10.1007/s10334-011-0290-7 ( reposiTUm)

Alfons, A., Templ, M., & Filzmoser, P. (2011). Simulation of close-to-reality population data for household surveys with application to EU-SILC. Statistical Methods & Applications, 20(3), 383–407. https://doi.org/10.1007/s10260-011-0163-2 ( reposiTUm)

Berger, W., Piringer, H., Filzmoser, P., & Gröller, E. (2011). Uncertainty-Aware Exploration of Continuous Parameter Spaces Using Multivariate Prediction. Computer Graphics Forum, 30(3), 911–920. http://hdl.handle.net/20.500.12708/164792 ( reposiTUm)

Alfons, A., Baaske, W. E., Filzmoser, P., Mader, W., & Wieser, R. (2011). Robust variable selection with application to quality of life research. Statistical Methods & Applications, 20(1), 65–82. https://doi.org/10.1007/s10260-010-0151-y ( reposiTUm)

Huf, W., Kalcher, K., Pail, G., Friedrich, M.-E., Filzmoser, P., & Kasper, S. (2011). Meta-analysis: Fact or fiction? How to interpret meta-analyses. The World Journal of Biological Psychiatry, 12(3), 188–200. https://doi.org/10.3109/15622975.2010.551544 ( reposiTUm)

Templ, M., Kowarik, A., & Filzmoser, P. (2011). Iterative stepwise regression imputation using standard and robust methods. Computational Statistics & Data Analysis, 55(10), 2793–2806. https://doi.org/10.1016/j.csda.2011.04.012 ( reposiTUm)

Varmuza, K., Engrand, C., Filzmoser, P., Hilchenbach, M., Kissel, J., Krüger, H., Silén, J., & Trieloff, M. (2011). Random projection for dimensionality reduction-Applied to time-of-flight secondary ion mass spectrometry data. Analytica Chimica Acta, 705(1–2), 48–55. https://doi.org/10.1016/j.aca.2011.03.031 ( reposiTUm)

Filzmoser, P., & Todorov, V. (2011). Review of robust multivariate statistical methods in high dimension. Analytica Chimica Acta, 705(1–2), 2–14. https://doi.org/10.1016/j.aca.2011.03.055 ( reposiTUm)

Todorov, V., Templ, M., & Filzmoser, P. (2011). Detection of multivariate outliers in business survey data with incomplete information. Advances in Data Analysis and Classification, 5(1), 37–56. https://doi.org/10.1007/s11634-010-0075-2 ( reposiTUm)

Sucharová, J., Suchara, I., Reimann, C., Boyd, R., Filzmoser, P., & Englmaier, P. (2011). Spatial distribution of lead and lead isotopes in soil B-horizon, forest-floor humus, grass (Avenella flexuosa) and spruce (Picea abies) needles across the Czech Republic. Applied Geochemistry, 26(7), 1205–1214. https://doi.org/10.1016/j.apgeochem.2011.04.009 ( reposiTUm)

Sarbu, A., Janauer, G., Schmidt-Mumm, U., Filzmoser, P., Smarandache, D., & Pascale, G. (2011). Characterisation of the potamal Danube River and the Delta: connectivity determines indicative macrophyte assemblages. Hydrobiologia, 671(1), 75–93. http://hdl.handle.net/20.500.12708/162127 ( reposiTUm)

Sucharova, J., Suchara, I., Hola, M., Reimann, C., Boyd, R., Filzmoser, P., & Englmaier, P. (2011). Linking chemical elements in forest floor humus (Oₕ-horizon) in the Czech Republic to contamination sources. Environmental Pollution, 159(5), 1205–1214. https://doi.org/10.1016/j.envpol.2011.01.041 ( reposiTUm)

Suchara, I., Sucharova, J., Hola, M., Reimann, C., Boyd, R., Filzmoser, P., & Englmaier, P. (2011). The performance of moss, grass, and 1- and 2-year old spruce needles as bioindicators of contamination: A comparative study at the scale of the Czech Republic. Science of the Total Environment, 409(11), 2281–2297. https://doi.org/10.1016/j.scitotenv.2011.02.003 ( reposiTUm)

Turkay, C., Filzmoser, P., & Hauser, H. (2011). Brushing Dimensions - A Dual Visual Analysis Model for High-Dimensional Data. IEEE Transactions on Visualization and Computer Graphics, 17(12), 2591–2599. https://doi.org/10.1109/TVCG.2011.178 ( reposiTUm)

Karacsony, Z., & Filzmoser, P. (2010). Asymptotic normality of kernel type regression estimators for random fields. Journal of Statistical Planning and Inference, 140, 872–886. http://hdl.handle.net/20.500.12708/166365 ( reposiTUm)

Kehrer, J., Filzmoser, P., & Hauser, H. (2010). Brushing Moments in Interactive Visual Analysis. Eurograhics Digital Library, 29(3), 10. http://hdl.handle.net/20.500.12708/167123 ( reposiTUm)

Todorov, V., & Filzmoser, P. (2010). Robust statistic for the one-way MANOVA. Computational Statistics & Data Analysis, 54, 37–48. http://hdl.handle.net/20.500.12708/166878 ( reposiTUm)

Alfons, A., Templ, M., & Filzmoser, P. (2010). An object-oriented framework for statistical simulation: The R Package simFrame. Journal of Statistical Software, 37(3), 1–36. http://hdl.handle.net/20.500.12708/167428 ( reposiTUm)

Hron, K., Templ, M., & Filzmoser, P. (2010). Imputation of missing values for compositional data using classical and robust methods. Computational Statistics & Data Analysis, 54, 3095–3107. http://hdl.handle.net/20.500.12708/167057 ( reposiTUm)

Filzmoser, P., Hron, K., & Reimann, R. (2010). The bivariate statistical analysis of environmental (compositional) data. Science of the Total Environment, 408, 4230–4238. http://hdl.handle.net/20.500.12708/167056 ( reposiTUm)

Reimann, C., Birke, M., & Filzmoser, P. (2010). Bottled drinking water: Water contamination from bottle materials (glass, hard PET, soft PET), the influence of colour and acidification. Applied Geochemistry, 25, 1030–1046. http://hdl.handle.net/20.500.12708/166917 ( reposiTUm)

Treiblmaier, H., & Filzmoser, P. (2010). Exploratory factor analysis revisited: How robust methods support the detection of hidden multivariate data structures in IS research. INFORMATION & MANAGEMENT, 47, 197–207. http://hdl.handle.net/20.500.12708/166915 ( reposiTUm)

Varmuza, K., Filzmoser, P., & Liebmann, B. (2010). Random projection experiments with chemometric data. Journal of Chemometrics, 24(3–4), 209–217. https://doi.org/10.1002/cem.1295 ( reposiTUm)

Reimann, C., Birke, M., & Filzmoser, P. (2010). Reply to the comment “Bottled drinking water: Water contamination from bottle materials (glass, hard PET, soft PET), the influence of colour and acidification” by Hayo Müller-Simon. Applied Geochemistry, 25, 1464–1465. http://hdl.handle.net/20.500.12708/167124 ( reposiTUm)

Boeheim, K., Pock, S.-M., Schloegel, M., & Filzmoser, P. (2010). Active Middle Ear Implant Compared With Open-Fit Hearing Aid in Sloping High-Frequency Sensorineural Hearing Loss. OTOLOGY & NEUROTOLOGY, 31(3), 424–429. http://hdl.handle.net/20.500.12708/166886 ( reposiTUm)

Liebmann, B., Filzmoser, P., & Varmuza, K. (2010). Robust and classical PLS regression compared. Journal of Chemometrics, 24(3–4), 111–120. https://doi.org/10.1002/cem.1279 ( reposiTUm)

Filzmoser, P. (2009). Invariant co-ordinate selection (Discussion on this paper). Journal of the Royal Statistical Society, 71(3), 549–592. http://hdl.handle.net/20.500.12708/165978 ( reposiTUm)

Filzmoser, P., Hron, K., & Reimann, C. (2009). Principal component analysis for compositional data with outliers. Environmetrics, 20, 621–632. http://hdl.handle.net/20.500.12708/165919 ( reposiTUm)

Todorov, V., & Filzmoser, P. (2009). An Object-Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. http://hdl.handle.net/20.500.12708/165918 ( reposiTUm)

Filzmoser, P., & Hron, K. (2009). Correlation Analysis for Compositional Data. Mathematical Geosciences, 41, 905–919. http://hdl.handle.net/20.500.12708/166766 ( reposiTUm)

Filzmoser, P., & Hron, K. (2009). Univariate statistical analysis of environmental (compositional) data: Problems and possibilities. Science of the Total Environment, 407, 6100–6108. http://hdl.handle.net/20.500.12708/166767 ( reposiTUm)

Filzmoser, P., Hron, K., Reimann, C., & Garrett, R. G. (2009). Robust factor analysis for compositional data. Computers and Geosciences, 35, 1854–1861. http://hdl.handle.net/20.500.12708/166768 ( reposiTUm)

Filzmoser, P., Liebmann, B., & Varmuza, K. (2009). Repeated double cross validation. Journal of Chemometrics, 23, 160–171. http://hdl.handle.net/20.500.12708/165478 ( reposiTUm)

Germ, M., Urbanc-Bercic, O., Janauer, G., Filzmoser, P., Exler, N., & Gaberscik, A. (2008). Macrophyte distribution pattern in the Krka River--the role of habitat quality. Large Rivers, 18(1-2), 145–155. http://hdl.handle.net/20.500.12708/170708 ( reposiTUm)

Croux, C., Filzmoser, P., & Joossens, K. (2008). Classification efficiencies for robust linear discriminant analysis. Statistica Sinica, 18, 581–599. http://hdl.handle.net/20.500.12708/170371 ( reposiTUm)

Filzmoser, P., Maronna, R., & Werner, M. (2008). Outlier identification in high dimensions. Computational Statistics & Data Analysis, 52, 1694–1711. http://hdl.handle.net/20.500.12708/170232 ( reposiTUm)

Templ, M., Filzmoser, P., & Reimann, C. (2008). Cluster analysis applied to regional geochemical data: Problems and possibilities. Applied Geochemistry, 23(8), 2198–2213. http://hdl.handle.net/20.500.12708/170300 ( reposiTUm)

Filzmoser, P., & Hron, K. (2008). Outlier Detection for Compositional Data Using Robust Methods. Mathematical Geosciences, 40(3), 233–248. http://hdl.handle.net/20.500.12708/170374 ( reposiTUm)

Weisser, A., Endel, G., Filzmoser, P., & Gyimesi, M. (2008). ATC-#gtICD - evaluating the reliability of prognoses for ICD-10 diagnoses derived from the ATC-Code of prescriptions. BMC Health Services Research, 8, 10. http://hdl.handle.net/20.500.12708/171232 ( reposiTUm)

Croux, C., Filzmoser, P., & Oliveira, M. R. (2007). Algorithms for Projection-Pursuit robust principal component analysis. Chemometrics and Intelligent Laboratory Systems, 87, 218–225. http://hdl.handle.net/20.500.12708/168719 ( reposiTUm)

Neykov, N., Filzmoser, P., Dimova, R., & Neytchev, P. (2007). Robust fitting of mixtures using the trimmed likelihood estimator. Computational Statistics & Data Analysis, 52, 299–308. http://hdl.handle.net/20.500.12708/168718 ( reposiTUm)

Reimann, C., Arnoldussen, A., Englmaier, P., Filzmoser, P., Finne, T. E., Garrett, R. G., Koller, F., & Nordgulen, O. (2007). Element concentrations and variations along a 120-km transect in southern Norway - Anthropogenic vs. geogenic vs. biogenic element sources and cycles. Applied Geochemistry, 22, 851–871. http://hdl.handle.net/20.500.12708/168740 ( reposiTUm)

Croux, C., & Filzmoser, P. (2007). A survey of robust statistics. Statistical Methods & Applications, 15, 280–282. http://hdl.handle.net/20.500.12708/168741 ( reposiTUm)

Richter, B., Gwechenberger, M., Filzmoser, P., Marx, M., Lercher, P., & Gössinger, H. D. (2006). Is inducibility of atrial fibrillation after radio frequency ablation really a relevant prognostic factor? European Heart Journal, 27, 2553–2559. http://hdl.handle.net/20.500.12708/172070 ( reposiTUm)

Fazekas, I., & Filzmoser, P. (2006). A Functional Central Limit Theorem for Kernel Type Density Estimators. Austrian Journal of Statistics, 35(4), 409–418. http://hdl.handle.net/20.500.12708/172132 ( reposiTUm)

Zick, D., Gassner, H., Filzmoser, P., Wanzenböck, J., Pamminger-Lahnsteiner, B., & Tischler, G. (2006). Changes in the fish species composition of all Austrian lakes #gt50 ha during the last 150 years. Fisheries Management and Ecology, 13, 103–111. http://hdl.handle.net/20.500.12708/172013 ( reposiTUm)

Filzmoser, P. (2005). Identification of Multivariate Outliers: A Performance Study. Austrian Journal of Statistics, 34, 2, 127–138. http://hdl.handle.net/20.500.12708/171884 ( reposiTUm)

Branco, J., Croux, C., Filzmoser, P., & Oliveira, M. R. (2005). Robust Canonical Correlations: A Comparative Study. Computational Statistics, 20, 203–229. http://hdl.handle.net/20.500.12708/171883 ( reposiTUm)

Serneels, S., Croux, C., Filzmoser, P., & Van Espen, P. J. (2005). Partial robust M-regression. Chemometrics and Intelligent Laboratory Systems, 79, 55–64. http://hdl.handle.net/20.500.12708/171891 ( reposiTUm)

Serneels, S., Croux, C., Filzmoser, P., & Van Espen, P. J. (2005). Robust continuum regression. Chemometrics and Intelligent Laboratory Systems, 76(2), 197–204. http://hdl.handle.net/20.500.12708/171892 ( reposiTUm)

Reimann, C., Filzmoser, P., & Garrett, R. G. (2005). Background and threshold: critical comparison of methods of determination. Science of the Total Environment, 346, 1–16. http://hdl.handle.net/20.500.12708/171886 ( reposiTUm)

Filzmoser, P., Garrett, R. G., & Reimann, C. (2005). Multivariate outlier detection in exploration geochemistry. COMPUTERS & GEOSCIENCES, 31, 579–587. http://hdl.handle.net/20.500.12708/171885 ( reposiTUm)

van Helvoort, P.-J., Filzmoser, P., & van Gaans, P. F. M. (2005). Sequential Factor Analysis as a new approach to multivariate analysis of heterogeneous geochemical datasets: An application to a bulk chemical characterization of fluvial deposits (Rhine-Meuse delta, The Netherlands. Applied Geochemistry, 20, 2233–2251. http://hdl.handle.net/20.500.12708/171912 ( reposiTUm)

Beiträge in Tagungsbänden

Oguamalam, J., Radojicic, U., & Filzmoser, P. (2024). Robust covariance estimation and functional anomaly detection based on the Minimum Regularized Covariance Trace estimator. In PROGRAM AND ABSTRACTS - Austrian Statistical Days 2024. Austrian Statistical Days 2024, Wien, Austria. ( reposiTUm)

Mayrhofer, M., Radojicic, U., & Filzmoser, P. (2024). A minimum covariance determinant approach for matrix-variate data. In Statistische Woche 2024: Book of Abstracts (pp. 88–88). ( reposiTUm)

Parzer, R., Filzmoser, P., & Vana Gür, L. (2024). Random projections for classification with high-dimensional data. In Proceedings of the 38th International Workshop on Statistical Modelling (pp. 236–239). ( reposiTUm)

Parzer, R., Vana Gür, L., & Filzmoser, P. (2024). Sparse data-driven random projection in regression for high-dimensional data. In P. Filzmoser (Ed.), Program and Abstracts: Austrian Statistical Days 2024 (pp. 11–11). ( reposiTUm)

Pfeiffer, P., & Filzmoser, P. (2024). Low-Rank Approximation of Data Matrices Using Robust Sparse Principal Component Analysis. In J. Ansari, S. Fuchs, W. Trutschnig, M. A. Lubiano Gomez, M. Á. Gil, P. Grzegorzewski, & O. Hryniewicz (Eds.), Combining, Modelling and Analyzing Imprecision, Randomness and Dependence (pp. 357–362). https://doi.org/10.1007/978-3-031-65993-5_44 ( reposiTUm)

Muehlmann, C., Filzmoser, P., & Nordhausen, K. (2024). Local Difference Matrices for Spatial Blind Source Separation. In S. Khomsi, M. Bezzeghoud, S. Banerjee, M. Eshagh, A. C. Benim, B. Merkel, A. Kallel, S. Panda, H. Chenchouni, S. Grab, & M. Barbieri (Eds.), Selected Studies in Geophysics, Tectonics and Petroleum Geosciences (pp. 63–65). https://doi.org/10.1007/978-3-031-43807-3_12 ( reposiTUm)

Puchhammer, P., & Filzmoser, P. (2023). A spatially smoothed MRCD estimator for local outlier detection. In ICORS 2023 - Book of Abstracts (pp. 58–59). ( reposiTUm)

Oguamalam, J., Radojicic, U., & Filzmoser, P. (2023). Functional Outlier Detection based on the Minimum Regularized Covariance Trace Estimator. In Book of abstracts: Joint conference of Data Science, Statistics & Visualisation and the European Conference on Data Analysis (pp. 102–102). ( reposiTUm)

Neubauer, L., & Filzmoser, P. (2023). A New Look at Model Averaging of Differently Sized Time Series. In DSSV 2023 : Book of Abstracts. DSSV-ECDA 2023, Antwerpen, Belgium. ( reposiTUm)

Mayrhofer, M., & Filzmoser, P. (2023). Explainable outlier detection based on Shapley values. In PROGRAMME AND ABSTRACTS 25th International Conference on Computational Statistics (COMPSTAT 2023) (pp. 13–14). ( reposiTUm)

Parzer, R., Vana Gür, L., & Filzmoser, P. (2023). Combining New Dimension Reduction Tools for High-Dimensional Regression. In DSSV 2023 : Book of Abstracts (pp. 103–103). ( reposiTUm)

Mayrhofer, M., Rieser, C., & Filzmoser, P. (2023). L0 Regularized Cellwise Outlier Detection and Covariance Estimation. In Book of abstracts: Joint conference of Data Science, Statistics & Visualisation and the European Conference on Data Analysis (pp. 95–95). ( reposiTUm)

Oguamalam, J., Radojicic, U., & Filzmoser, P. (2023). Minimum Regularized Covariance Trace Estimator and Outlier Detection for Functional Data. In Book of Abstracs - Olomoucian Days of Applied Mathematics ODAM 2023 (pp. 57–57). ( reposiTUm)

Filzmoser, P., & Mayrhofer, M. (2023). Outlier explanation based on Shapley values for vector- and matrix-valued observations. In P. Coretto, G. N. Giordano, & M. La Rocca (Eds.), CLADAC 2023 : Book of Abstracts and Short Papers : 14th Scientific Meeting of the Classification and Data Analysis Group (pp. 156–158). Pearson Education Resources. http://hdl.handle.net/20.500.12708/190505 ( reposiTUm)

Mayrhofer, M., & Filzmoser, P. (2023). Explainable Multivariate Outlier Detection based on Shapley Values. In Book of Abstracts Olomoucian Days of Applied Mathematics ODAM 2023 (pp. 53–53). ( reposiTUm)

Filzmoser, P., Mayrhofer, M., Radojicic, U., & Lewitschnig, H. (2023). Explainable outlier identification for matrix-valued observations. In Book of Abstracts : International Conference on Data Science : ICDS 2023 : Multidimensional Perspectives: From Statistical Learning to Data Science Applications (pp. 13–13). http://hdl.handle.net/20.500.12708/190530 ( reposiTUm)

Lietzen, N., Virta, J., Nordhausen, K., & Ilmonen, P. (2019). Minimum Distance Index for Non-Square Complex Valued Mixing Matrices. In P. Filzmoser & Y. Kharin (Eds.), Proceedings of the 12th International Conference on Computer Data Analysis and Modeling (pp. 79–86). Minsk Publishing Center BSU. http://hdl.handle.net/20.500.12708/41694 ( reposiTUm)

Miksova, D., Rieser, C., & Filzmoser, P. (2019). Identification of mineralization in geochemistry based on the spatial curvature of log-ratios. In Identification of mineralization in geochemistry based on the spatial curvature of log-ratios (pp. 246–248). http://hdl.handle.net/20.500.12708/41705 ( reposiTUm)

Crocetti, L., Dorigo, W., Martens, B., & Filzmoser, P. (2019). Identifying oceanic-atmospheric controls on hydrology using a machine learning approach. In EGU General Assembly 2019. EGU General Assembly 2019, Vienna, Austria. Copernicus Publications. http://hdl.handle.net/20.500.12708/43952 ( reposiTUm)

Wurl, A., Falkner, A., Haselböck, A., Mazak, A., & Filzmoser, P. (2019). Exploring robustness in a combined feature selection approach. In Proceedings of the 8th International Conference on Data Science, Technology and Applications. Scitepress - Science and Technology Publications, LDA. https://doi.org/10.5220/0007924400840091 ( reposiTUm)

Filzmoser, P. (2017). Correlation between variables in compositional data. In EGU General Assembly 2017 (p. 1). Copernicus Publications. http://hdl.handle.net/20.500.12708/122044 ( reposiTUm)

Varmuza, K., Brandstätter, F., Cottin, H., Engrand, C., Ferrière, L., Filzmoser, P., Fray, N., Hilchenbach, M., Hoffmann, I., Kissel, J., Koeberl, C., Modica, P., Paquette, J., & Stenzel, O. (2017). Elemental surface composition of comet 67P grains (Rosetta) and of carbonaceous chondrite meteorites - characterized by multivariate mass spectral data (COSIMA). In EGU General Assembly 2017 (p. 1). Copernicus Publications. http://hdl.handle.net/20.500.12708/122039 ( reposiTUm)

Grad-Gyenge, L., & Filzmoser, P. (2017). The paradigm of relatedness. In W. Abramowicz, R. Alt, & B. Franczyk (Eds.), Business Information Systems Workshops BIS 2016 International Workshops, Leipzig, Germany, July 6-8, 2016, Revised Papers (pp. 57–68). Springer. https://doi.org/10.1007/978-3-319-52464-1_6 ( reposiTUm)

Brodinova, S., Filzmoser, P., Ortner, T., Zaharieva, M., & Breiteneder, C. (2017). Robust and sparse clustering for high-dimensional data. In CLADAG 2017 Book of Short Papers. Conference of the CLAssification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS), Mailand, Italy. http://hdl.handle.net/20.500.12708/57014 ( reposiTUm)

Grad-Gyenge, L., & Filzmoser, P. (2016). Recommendation Techniques on a Knowledge Graph for Email Remarketing. In eKNOW 2016, The Eighth International Conference on Information, Process, and Knowledge Management (pp. 51–56). IARIA. http://hdl.handle.net/20.500.12708/41458 ( reposiTUm)

Brodinova, S., Ortner, T., Filzmoser, P., Zaharieva, M., & Breiteneder, C. (2016). Evaluation of robust PCA for supervised audio outlier detection. In Proceeding of 22nd International Conference on Computational Statistics (COMPSTAT) (p. 12). http://hdl.handle.net/20.500.12708/56525 ( reposiTUm)

Bögl, M., Aigner, W., Filzmoser, P., Gschwandtner, T., Lammarsch, T., Miksch, S., & Rind, A. (2015). Integrating Predictions in Time Series Model Selection. In J. Yang, E. Bertini, N. Elmqvist, T. Dwyer, X. Yuan, & H. Carr (Eds.), EuroVA 2015 EuroVis Workshop on Visual Analytics (pp. 73–78). The Eurographics Association. https://doi.org/10.2312/eurova.20151107 ( reposiTUm)

Brodinova, S., Ortner, T., Filzmoser, P., Zaharieva, M., & Breiteneder, C. (2015). Simulation of Robust PCA for Supervised Audio Outlier Detection. In Eighth International Workshop on Simulation: Book of Abstracts. International Workshop on Simulation, Vienna, Austria. http://hdl.handle.net/20.500.12708/56521 ( reposiTUm)

Hoffmann, I., Filzmoser, P., & Serneels, S. (2015). Robust and sparse PLS for binary classification. In International Conference on Robust Statistics - Book of Abstracts (pp. 22–23). http://hdl.handle.net/20.500.12708/41392 ( reposiTUm)

Filzmoser, P. (2015). Robust statistics and R. In International Conference on Robust Statistics - Book of Abstracts. ICORS 2015 International Conference on Robust Statistics, Indian Statistical Institute, Kolkata, India, Non-EU. http://hdl.handle.net/20.500.12708/41393 ( reposiTUm)

Bögl, M., Aigner, W., Filzmoser, P., Gschwandtner, T., Lammarsch, T., Miksch, S., & Rind, A. (2015). Visually and Statistically Guided Imputation of Missing Values in Univariate Seasonal Time Series. In J. Yang, E. Bertini, N. Elmqvist, T. Dwyer, X. Yuan, & H. Carr (Eds.), Poster Proceedings of the IEEE Visualization Conference 2015 (p. 2). http://hdl.handle.net/20.500.12708/56130 ( reposiTUm)

Grad-Gyenge, L., Filzmoser, P., & Werthner, H. (2015). Recommendations on a Knowledge Graph. In MLRec 2015 : 1st International Workshop on Machine Learning Methods for Recommender Systems (pp. 13–20). http://hdl.handle.net/20.500.12708/56430 ( reposiTUm)

Bögl, M., Aigner, W., Filzmoser, P., Gschwandtner, T., Lammarsch, T., Miksch, S., & Rind, A. (2014). Visual Analytics Methods to Guide Diagnostics for Time Series Model Predictions. In Proceedings of the 2014 IEEE VIS Workshop on Visualization for Predictive Analytics (p. 4). http://hdl.handle.net/20.500.12708/55730 ( reposiTUm)

Filzmoser, P., Brito, P., & Pedro Duarte Silva, A. (2014). Outlier detection in interval data. In M. Gilli, G. Gonzalez-Rodriguez, & A. Nieto-Reyes (Eds.), Proceedings of COMPSTAT 2014 (p. 11). http://hdl.handle.net/20.500.12708/41824 ( reposiTUm)

Reimann, C., Filzmoser, P., & Hron, K. (2014). Geochemical mapping and CoDa: Problems and Possibilities. In K. Hron & P. Filzmoser (Eds.), GeoMap Workshop Proceedings (pp. 47–49). Palacký University, Olomouc, Cz. http://hdl.handle.net/20.500.12708/41823 ( reposiTUm)

Reitner, H., Filzmoser, P., & Pirkl, H. (2014). Subject to Change: A log-ra! o approach to the geochemistry of stream sediment samples. In K. Hron & P. Filzmoser (Eds.), GeoMap Workshop Proceedings (pp. 50–52). Palacký University, Olomouc, Cz. http://hdl.handle.net/20.500.12708/41343 ( reposiTUm)

Kalivodova, A., Hron, K., & Filzmoser, P. (2014). PLS-DA for metabolomical (compositional) data using the logratio approach. In CFE-ERCIM 2014 - Book of Abstracts. ERCIM 2014 - 7th International Conference of the ERCIM WG on Computational and Methodological Statistics, Pisa, Italy. http://hdl.handle.net/20.500.12708/41302 ( reposiTUm)

Stenzel, O., Varmuza, K., Engrand, C., Ferrière, L., Brandstätter, F., Koeberl, C., Filzmoser, P., & Hilchenbach, M. (2014). Characterisation of meteoritic samples with the Rosetta Cosima TOF-SIMS laboratory reference model - a covariance approach. In Asteroids, Comets, Meteors, Book of Abstracts, Helsinki, Finland, 2014 Editors: K. Muinonen, A. Penttilä, M. Granvik, A. Virkki, G. Fedorets, O. Wilkman, T. Kohout. ACM 2014 - Asteroids, Comets, Meteors, Helsinki, Finland. http://hdl.handle.net/20.500.12708/41310 ( reposiTUm)

Nordhausen, K., Oja, H., Filzmoser, P., & Reimann, C. (2014). Blind source separation for spatial compositional data. In CFE-ERCIM 2014 - Book of Abstracts. ERCIM 2014 - 7th International Conference of the ERCIM WG on Computational and Methodological Statistics, Pisa, Italy. http://hdl.handle.net/20.500.12708/41335 ( reposiTUm)

Mert, M. C., Filzmoser, P., & Hron, K. (2014). Error propagation of compositional data transformations. In CFE-ERCIM 2014 - Book of Abstracts. ERCIM 2014 - 7th International Conference of the ERCIM WG on Computational and Methodological Statistics, Pisa, Italy. http://hdl.handle.net/20.500.12708/41336 ( reposiTUm)

Brito, P., Filzmoser, P., & Hron, K. (2014). Statistical analysis of interval compositional data. In CFE-ERCIM 2014 - Book of Abstracts. ERCIM 2014 - 7th International Conference of the ERCIM WG on Computational and Methodological Statistics, Pisa, Italy. http://hdl.handle.net/20.500.12708/41337 ( reposiTUm)

Hron, K., Menafoglio, A., Templ, M., Hruzova, K., & Filzmoser, P. (2014). Simplicial principal component analysis for density functions in Bayes spaces. In CFE-ERCIM 2014 - Book of Abstracts. ERCIM 2014 - 7th International Conference of the ERCIM WG on Computational and Methodological Statistics, Pisa, Italy. http://hdl.handle.net/20.500.12708/41338 ( reposiTUm)

Kynčlová, P., Filzmoser, P., & Hron, K. (2014). Application of T-spaces in modeling compositional time series. In CFE-ERCIM 2014 - Book of Abstracts. ERCIM 2014 - 7th International Conference of the ERCIM WG on Computational and Methodological Statistics, Pisa, Italy. http://hdl.handle.net/20.500.12708/41339 ( reposiTUm)

Serneels, S., Filzmoser, P., Hoffmann, I., & Croux, C. (2014). Sparse partial robust M regression. In ICORS14 - Conference Guide & Book of Abstracts (p. 24). http://hdl.handle.net/20.500.12708/41344 ( reposiTUm)

Neykov, N., Filzmoser, P., & Neytchev, P. (2014). Robust variable selection in joint modeling of location, scale and shape for high dimensional data through trimming (CS6). In ICORS14 - Conference Guide & Book of Abstracts (p. 39). http://hdl.handle.net/20.500.12708/41345 ( reposiTUm)

Gschwandtner, M., & Filzmoser, P. (2013). Outlier Detection in High Dimension Using Regularization. In Synergies of Soft Computing and Statistics for Intelligent Data Analysis (pp. 237–244). Springer Verlag Berlin-Heidelberg. https://doi.org/10.1007/978-3-642-33042-1_26 ( reposiTUm)

Templ, M., van den Boogaart, G., Eichler, J., Filzmoser, P., Hron, K., & Tolosana-Delgado, R. (2013). Software compositionsGUI. In K. Hron, P. Filzmoser, & M. Templ (Eds.), Proceedings of the 5th International Workshop on Compositional Data Analysis CoDaWork 2013 June 3-7, 2013, Vorau, Austria. http://hdl.handle.net/20.500.12708/41241 ( reposiTUm)

Templ, M., Hron, K., & Filzmoser, P. (2013). The R packages robCompositions and compositionsGUI. In K. Hron, P. Filzmoser, & M. Templ (Eds.), Proceedings of the 5th International Workshop on Compositional Data Analysis CoDaWork 2013 June 3-7, 2013, Vorau, Austria. http://hdl.handle.net/20.500.12708/41240 ( reposiTUm)

Mert, M. C., Filzmoser, P., & Hron, K. (2013). Sparse Principal Balances for High-Dimensional Compositional Data. In Proceedings of the 10th International Conderence Computer Data Analysis & Modelling 2013. CDAM 2013 10th International Conference Computer Data Analysis & Modeling 2013 Theoretical & Applied Stochastics, Minsk, Weißrussland, Belarus, Non-EU. Publishing Center of BSU Minsk 2013. http://hdl.handle.net/20.500.12708/41245 ( reposiTUm)

Filzmoser, P. (2013). R Tools for Robust Statistical Analysis of High-Dimensional Data. In Proceedings of the 10th International Conderence Computer Data Analysis & Modelling 2013. CDAM 2013 10th International Conference Computer Data Analysis & Modeling 2013 Theoretical & Applied Stochastics, Minsk, Weißrussland, Belarus, Non-EU. http://hdl.handle.net/20.500.12708/41246 ( reposiTUm)

Filzmoser, P. (2013). Opening Lecture: A Projection-Pursuit Method for Sparse Robust PCA. In Proceedings of the 10th International Conderence Computer Data Analysis & Modelling 2013. CDAM 2013 10th International Conference Computer Data Analysis & Modeling 2013 Theoretical & Applied Stochastics, Minsk, Weißrussland, Belarus, Non-EU. http://hdl.handle.net/20.500.12708/41243 ( reposiTUm)

Monti, G., Hron, K., Filzmoser, P., & Templ, M. (2013). Covariance-Based Outlier Detection for Compositional Data with Structural Zeros: Application to Italian Survey of Household Income and Wealth Data. In D. Vita e Pensiero (Ed.), Dipartimento di Economia, Metodi Quantitativi e Strategie di Impresa. Vita e Pensiero. http://hdl.handle.net/20.500.12708/41247 ( reposiTUm)

Brandl, M., Filzmoser, P., & Hauzenberger, C. (2013). Northern Alpine versus Carpatian radiolarites - a case study from the Upper Palaeolithic Krems-Wachtberg site (Lower Austria). In K. Hron, P. Filzmoser, & M. Templ (Eds.), Proceedings of the 5th International Workshop on Compositional Data Analysis CoDaWork 2013 June 3-7, 2013, Vorau, Austria (p. 16). http://hdl.handle.net/20.500.12708/41254 ( reposiTUm)

Filzmoser, P. (2013). Compositional data and R. In K. Hron, P. Filzmoser, & M. Templ (Eds.), Proceedings of the 5th International Workshop on Compositional Data Analysis CoDaWork 2013 June 3-7, 2013, Vorau, Austria. http://hdl.handle.net/20.500.12708/41253 ( reposiTUm)

Zehetgruber, J., Filzmoser, P., Hron, K., & Templ, M. (2013). Robust multivariate regression with compositional data. In K. Hron, P. Filzmoser, & M. Templ (Eds.), Proceedings of the 5th International Workshop on Compositional Data Analysis CoDaWork 2013 June 3-7, 2013, Vorau, Austria (p. 65). http://hdl.handle.net/20.500.12708/41256 ( reposiTUm)

Kynčlová, P., Filzmoser, P., & Hron, K. (2013). Compositional time series: The VAR model. In K. Hron, P. Filzmoser, & M. Templ (Eds.), Proceedings of the 5th International Workshop on Compositional Data Analysis CoDaWork 2013 June 3-7, 2013, Vorau, Austria (p. 39). http://hdl.handle.net/20.500.12708/41255 ( reposiTUm)

Donevska, S., Fiserová, E., Filzmoser, P., & Hron, K. (2013). Covariance-based variable selection for compositional data. In K. Hron, P. Filzmoser, & M. Templ (Eds.), Proceedings of the 5th International Workshop on Compositional Data Analysis CoDaWork 2013 June 3-7, 2013, Vorau, Austria (p. 23). http://hdl.handle.net/20.500.12708/41258 ( reposiTUm)

Reimann, C., Filzmoser, P., & Hron, K. (2013). Challenges for CoDa in geochemical practice. In K. Hron, P. Filzmoser, & M. Templ (Eds.), Proceedings of the 5th International Workshop on Compositional Data Analysis CoDaWork 2013 June 3-7, 2013, Vorau, Austria (p. 52). http://hdl.handle.net/20.500.12708/41257 ( reposiTUm)

Templ, M. (2013). Supporting tools. In K. Hron, P. Filzmoser, & M. Templ (Eds.), Proceedings of the 5th International Workshop on Compositional Data Analysis CoDaWork 2013 June 3-7, 2013, Vorau, Austria. http://hdl.handle.net/20.500.12708/41260 ( reposiTUm)

Mert, M. C., Filzmoser, P., & Hron, K. (2013). Sparse principal balances. In K. Hron, P. Filzmoser, & M. Templ (Eds.), Proceedings of the 5th International Workshop on Compositional Data Analysis CoDaWork 2013 June 3-7, 2013, Vorau, Austria (pp. 44–45). http://hdl.handle.net/20.500.12708/41288 ( reposiTUm)

Filzmoser, P. (2013). Analysis of chemical data from a compositional point of view. In VIII Colloquium Chemometricum Mediterraneum (p. 8). http://hdl.handle.net/20.500.12708/41285 ( reposiTUm)

Aivazian, S., Filzmoser, P., & Kharin, Y. (2013). Greetings from the Programm Committee Co-Chairs. In S. Aivazian, P. Filzmoser, & Y. Kharin (Eds.), Computer Data Analysis and Modeling - Theoretical and Applied Stochastics / Proceedings of the Tenth International Conference Minsk, September 10-14, 2013 (pp. 5–6). Publishing center BSU, Minsk. http://hdl.handle.net/20.500.12708/41282 ( reposiTUm)

Todorov, V., Facevicova, K., Hron, K., Guo, D., & Templ, M. (2013). Statistical analysis of compositional 2x2 tables with an economic application. In K. Hron, P. Filzmoser, & M. Templ (Eds.), Proceedings of the 5th International Workshop on Compositional Data Analysis CoDaWork 2013 June 3-7, 2013, Vorau, Austria (p. 58). http://hdl.handle.net/20.500.12708/41819 ( reposiTUm)

Templ, M., Hron, K., Filzmoser, P., & Monti, G. (2013). Methods to Detect Outliers in Compositional Data with Structural Zeros. In K. Hron, P. Filzmoser, & M. Templ (Eds.), Proceedings of the 5th International Workshop on Compositional Data Analysis CoDaWork 2013 June 3-7, 2013, Vorau, Austria (p. 56). http://hdl.handle.net/20.500.12708/41820 ( reposiTUm)

van den Boogaart, G., Tolosana-Delgado, R., Hron, K., Templ, M., & Filzmoser, P. (2013). Compositional regression with unobserved components or below detection limit values. In K. Hron, P. Filzmoser, & M. Templ (Eds.), Proceedings of the 5th International Workshop on Compositional Data Analysis CoDaWork 2013 June 3-7, 2013, Vorau, Austria (p. 59). http://hdl.handle.net/20.500.12708/41821 ( reposiTUm)

Schroeder, F., Braumann, A., Filzmoser, P., & Hron, K. (2013). Robust variable selection in linear regression with compositional explanatory variables. In K. Hron, P. Filzmoser, & M. Templ (Eds.), Proceedings of the 5th International Workshop on Compositional Data Analysis CoDaWork 2013 June 3-7, 2013, Vorau, Austria (p. 55). http://hdl.handle.net/20.500.12708/41259 ( reposiTUm)

Pascoal, C., de Oliviera, M. R., Valades, R., Filzmoser, P., Salvador, P., & Pacheco, A. (2012). Robust feature selection and robust PCA for internet traffic anomaly detection. In 2012 Proceedings IEEE INFOCOM. 31th International Conference on Computer Communications IEEE, Orlando / Florida, Non-EU. https://doi.org/10.1109/infcom.2012.6195548 ( reposiTUm)

Templ, M., Hulliger, B., & Kowarik, A. (2012). Visualization of regional indicators with the checkerplot. In P. Filzmoser (Ed.), Book of Abstracts (p. 141). http://hdl.handle.net/20.500.12708/41147 ( reposiTUm)

Filzmoser, P., Croux, C., & Fritz, H. (2012). Robust sparse PCA in R. In Proceedings COMPSTAT2012. International Conference on Computational Statistics, Limassol / Cyprus, EU. http://hdl.handle.net/20.500.12708/41216 ( reposiTUm)

Martin-Fernandez, J. A., Hron, K., Templ, M., Filzmoser, P., & Palarea-Albaladejo, J. (2012). Evaluating the performance of a Bayesian-multiplicative treatment of zeros in compositional data sets. In Proceedings of COMPSTAT2012. International Conference on Computational Statistics, Limassol / Cyprus, EU. http://hdl.handle.net/20.500.12708/41221 ( reposiTUm)

Kehrer, J., Boubela, R. N., Filzmoser, P., & Piringer, H. (2012). A generic model for the integration of interactive visualization and statistical computing using R. In 2012 IEEE Conference on Visual Analytics Science and Technology (VAST). IEEE Conference on Visual Analytics Science and Technology, VAST 2012, Seattle, WA, USA, Non-EU. https://doi.org/10.1109/vast.2012.6400537 ( reposiTUm)

Varmuza, K., Liebmann, B., & Filzmoser, P. (2012). Repeated double cross validation for optimization and evaluation of empirical classifiers. In XIII Chemometrics in Analytical Chemistry (p. 160). http://hdl.handle.net/20.500.12708/47810 ( reposiTUm)

Liebmann, B., Todeschini, R., Consonni, V., Filzmoser, P., & Varmuza, K. (2012). Variable Selection by the LASSO method. In XIII Chemometrics in Analytical Chemistry (p. 161). http://hdl.handle.net/20.500.12708/47809 ( reposiTUm)

Katschnig, H., Endel, G., Endel, F., Filzmoser, P., & Weibold, B. (2011). Identifying psychiatric patients’ pathways of care by record linkage after pseudonymisation: linking inpatient and outpatient data for the total population of a province of Austria. In Psychiatrische Praxis. Society for Psychotherapy Research. https://doi.org/10.1055/s-0031-1277820 ( reposiTUm)

Treiblmaier, H., & Filzmoser, P. (2011). Benefits from using continuous rating scales in online survey research. In Proceedings of the International Conference on Information Systems (p. 13). Association for Information Systems. http://hdl.handle.net/20.500.12708/41128 ( reposiTUm)

Varmuza, K., Filzmoser, P., & Liebmann, B. (2011). Repeated double cross validation for classification models. In Conferentia Chemometrica 2011. Conferentia Chemometrica 2011, Sümeg, Ungarn, EU. http://hdl.handle.net/20.500.12708/47407 ( reposiTUm)

Templ, M., Alfons, A., Filzmoser, P., & Holzer, J. (2011). Estimation of Social Inclusion Indicators with Bounded Influence of Incomes by Tail Modeling. In Book of Abstracts of the Olomoucian Days of Applied Mathematics (p. 61). http://hdl.handle.net/20.500.12708/41055 ( reposiTUm)

Hron, K., Fiserová, E., Filzmoser, P., & Templ, M. (2011). On the Interpretation of Orthonormal Coordinates for Compositional Data with Applications. In Book of Abstracts of the Olomoucian Days of Applied Mathematics (p. 27). http://hdl.handle.net/20.500.12708/41054 ( reposiTUm)

Filzmoser, P., Hron, K., & Templ, M. (2011). Discriminant Analysis for Compositional Data and Robust Parameter Estimation. In Book of Abstracts of the Olomoucian Days of Applied Mathematics (p. 17). http://hdl.handle.net/20.500.12708/41053 ( reposiTUm)

Templ, M., Alfons, A., Kowarik, A., Meindl, B., Filzmoser, P., Hulliger, B., & Lussmann, D. (2011). Visualisation of indicators within the AMELI project. In Proceedings of the NTTS 2011 conference (p. 2). http://hdl.handle.net/20.500.12708/41057 ( reposiTUm)

Alfons, A., Templ, M., & Filzmoser, P. (2011). Robust estimation of social inclusion indicators based on Pareto tail modeling. In Proceedings of the NTTS 2011 conference (p. 3). http://hdl.handle.net/20.500.12708/41056 ( reposiTUm)

Hron, K., Templ, M., & Filzmoser, P. (2011). Classical and robust imputation of missing values for compositional data using balances. In Proceedings of the 4th International Workshop on Compositional Data Analysis (p. 1). http://hdl.handle.net/20.500.12708/41063 ( reposiTUm)

Filzmoser, P., Hron, K., & Templ, M. (2011). Robust compositional data analysis. In Proceedings of the 4th International Workshop on Compositional Data Analysis (p. 4). http://hdl.handle.net/20.500.12708/41064 ( reposiTUm)

Templ, M., Kowarik, A., & Filzmoser, P. (2011). Imputation of complex data with R-package VIM: traditional and new methods based on robust estimation. In Proceedings of the Conference of European Statisticians, Work Session on Statistical Data Editing (p. 10). http://hdl.handle.net/20.500.12708/41067 ( reposiTUm)

Todorov, V., Templ, M., & Filzmoser, P. (2011). Software for multivariate outlier detection in survey data. In Proceedings of the Conference of European Statisticians, Work Session on Statistical Data Editing (p. 16). http://hdl.handle.net/20.500.12708/41066 ( reposiTUm)

Templ, M., Filzmoser, P., & Hron, K. (2011). Analysis of Compositional Data using Robust Methods. The R-Package robCompositions. In Proceedings of the Conference of European Statisticians, Work Session on Statistical Data Editing. CODAWORK’11, Girona, EU. http://hdl.handle.net/20.500.12708/41068 ( reposiTUm)

Alfons, A., Templ, M., & Filzmoser, P. (2010). Exploring Microdata with Missing Information. In Abstracts of the Workshop on Exploratory Data Analysis and Visualization (p. 11). http://hdl.handle.net/20.500.12708/40940 ( reposiTUm)

Hron, K., Filzmoser, P., & Templ, M. (2010). Classical and robust simple random sampling for compositional data. In Abstracts of the International Conference on Indicators and Survey Methodology (p. 33). http://hdl.handle.net/20.500.12708/40939 ( reposiTUm)

Templ, M., Kovarik, A., Filzmoser, P., & Alfons, A. (2010). Iterative imputation of complex survey data using robust methods. In Abstracts of the International Conference on Indicators and Survey Methodology (p. 51). http://hdl.handle.net/20.500.12708/40937 ( reposiTUm)

Alfons, A., Kraft, S., Templ, M., & Filzmoser, P. (2010). Simulation of population data for complex household surveys. In Abstracts of the International Conference on Indicators and Survey Methodology (p. 13). http://hdl.handle.net/20.500.12708/40938 ( reposiTUm)

Filzmoser, P., & Templ, M. (2010). Data Analysis with Robust Statistical Methods. In Abstracts of the Workshop on Exploratory Data Analysis and Visualization (p. 17). http://hdl.handle.net/20.500.12708/40941 ( reposiTUm)

Varmuza, K., Filzmoser, P., Hilchenbach, M., Kissel, J., Krüger, H., & Silèn, J. (2010). Random projection for dimensionality reduction - applied to TOF-SIMS data relevant for future experiments near a comet. In CAC 2010 Book of Abstracts (p. 277). http://hdl.handle.net/20.500.12708/47034 ( reposiTUm)

Alfons, A., Templ, M., & Filzmoser, P. (2010). The R package simFrame: An object-oriented approach towards simulation studies in statistics. In Abstracts of Contributed Presentations (p. 13). http://hdl.handle.net/20.500.12708/40948 ( reposiTUm)

Templ, M., Hron, K., & Filzmoser, P. (2010). robCompositions: An R-package for robust statistical analysis of compositional data. In Abstracts of Contributed Presentations (p. 161). http://hdl.handle.net/20.500.12708/40949 ( reposiTUm)

Alfons, A., Templ, M., Filzmoser, P., Holzer, J., & Gonzalez-Rodriguez, G. (2010). A comparison of robust methods for Pareto tail modeling in the case of Laeken indicators. In C. Borgelt, G. Gonzalez-Rodriguez, W. Trutschnig, M. A. Lubiano, M. A. Gil, P. Grzegorzewski, & O. Hryniewicz (Eds.), Combining Soft Computing and Statistical Methods in Data Analysis (pp. 17–24). http://hdl.handle.net/20.500.12708/40969 ( reposiTUm)

Filzmoser, P., & Horn, K. (2010). Multivariate outlier detection with compositional data. In S. Aivazian, P. Filzmoser, & Y. Kharin (Eds.), Proceedings of the Ninth International Conference on Computer Data Analysis and Modeling (pp. 45–52). http://hdl.handle.net/20.500.12708/40966 ( reposiTUm)

Filzmoser, P., & Horn, K. (2010). Robust methods for compositional data. In G. Saporta & Y. Lechevallier (Eds.), Proceedings in Computational Statistics (pp. 79–88). http://hdl.handle.net/20.500.12708/40967 ( reposiTUm)

Alfons, A., Templ, M., & Filzmoser, P. (2010). Contamination models in the R package simFrame for statistical simulation. In S. Aivazian, P. Filzmoser, & Y. Kharin (Eds.), Proceedings of the Ninth International Conference on Computer Data Analysis and Modeling (pp. 178–181). http://hdl.handle.net/20.500.12708/40965 ( reposiTUm)

Hron, K., Templ, M., & Filzmoser, P. (2010). Exploratory compositional data analysis using the R-package robCompositions. In S. Aivazian, P. Filzmoser, & Y. Kharin (Eds.), Proceedings of the Ninth International Conference Data Analysis and Modeling (pp. 179–186). http://hdl.handle.net/20.500.12708/40968 ( reposiTUm)

Hron, K., & Filzmoser, P. (2010). Elements of robust regression for data with absolute and relative information. In C. Borgelt, G. Gonzalez-Rodriguez, W. Trutschnig, M. A. Lubiano, M. A. Gil, P. Grzegorzewski, & O. Hryniewicz (Eds.), Combining Soft Computing and Statistical Methods in Data Analysis (pp. 329–335). http://hdl.handle.net/20.500.12708/40974 ( reposiTUm)

Filzmoser, P. (2010). Soft methods in robust statistics. In C. Borgelt, G. Gonzalez-Rodriguez, W. Trutschnig, M. A. Lubiano, M. A. Gil, P. Grzegorzewski, & O. Hryniewicz (Eds.), Combining Soft Computing and Statistical Methods in Data Analysis (pp. 273–280). http://hdl.handle.net/20.500.12708/40973 ( reposiTUm)

Alfons, A., & Filzmoser, P. (2010). Robust model selection in the social sciences. In Programme and Abstracts: 4th CSDA International Conference on Computational and Financial Econometrics (p. 63). http://hdl.handle.net/20.500.12708/40987 ( reposiTUm)

Varmuza, K., Filzmoser, P., & Dehmer, M. (2010). Statistical evaluation of molecular descriptors used in quantitative-structure-activity relationships. In Programme and Abstracts (p. 55). http://hdl.handle.net/20.500.12708/47028 ( reposiTUm)

Alfons, A., Templ, M., & Filzmoser, P. (2009). On the influence of imputation methods on Laeken indicators: simulations and recommendations. In Proceedings of the Conference of European Statisticians (p. 9). http://hdl.handle.net/20.500.12708/40851 ( reposiTUm)

Todorov, V., Templ, M., & Filzmoser, P. (2009). Outlier detection in survey data using robust methods. In Proceedings of the Conference of European Statisticians (p. 11). http://hdl.handle.net/20.500.12708/40852 ( reposiTUm)

Templ, M., Filzmoser, P., & Hron, K. (2009). Imputation of item non-responses in compositional data using robust methods. In Proceedings of the Conference of European Statisticians (p. 11). http://hdl.handle.net/20.500.12708/40850 ( reposiTUm)

Filzmoser, P., Fritz, H., Horn, K., & Templ, M. (2009). The estimation of missing data in presence of outliers: computational aspects. In Abstracts of the Third International Conference on Computational and Financial Econometrics (CFE 09) and Second Workshop of the ERCIM Working Group on Computing & Statistics (ERCIM 09) (p. 29). http://hdl.handle.net/20.500.12708/40854 ( reposiTUm)

Templ, M., Kowarik, A., & Filzmoser, P. (2009). Iterative robust model-based Imputation of complex data. In Abstracts of the Third International Conference on Computational and Financial Econometrics (CFE 09) and Second Workshop of the ERCIM Working Group on Computing & Statistics (ERCIM) 09 (p. 29). http://hdl.handle.net/20.500.12708/40853 ( reposiTUm)

Vezzoli, G., Hron, K., & Filzmoser, P. (2009). A Statistical Approach to the Reconstruction of Pleistocene Drainage of the Po River Basin. In Abstracts of the International Conference on Robust Statistics (pp. 155–156). http://hdl.handle.net/20.500.12708/40871 ( reposiTUm)

Neykov, N., Filzmoser, P., & Neytchev, P. (2009). Statistical Estimators based on Trimming. In Abstracts of the International Conference on Robust Statistics (p. 112). http://hdl.handle.net/20.500.12708/40872 ( reposiTUm)

Filzmoser, P., & Hron, K. (2009). Robust Factor Analysis. In Abstracts of the International Conference on Robust Statistics (pp. 51–52). http://hdl.handle.net/20.500.12708/40870 ( reposiTUm)

Hron, K., & Filzmoser, P. (2009). A Robust Biplot for Compositional Data. In Abstracts of the International Conference on Robust Statistics (pp. 70–71). http://hdl.handle.net/20.500.12708/40873 ( reposiTUm)

Templ, M., Filzmoser, P., & Hron, K. (2009). Robust Imputation of Missing Values in Compositional Data Using the R-package robCompositions. In Proceedings of the NTTS Conference. NTTS Conference, Brüssel, EU. http://hdl.handle.net/20.500.12708/40835 ( reposiTUm)

Templ, M., Filzmoser, P., & Hron, K. (2009). Robustness for Compositional Data Using the R Package robCompositions. In Abstracts of the International Conference in Robust Statistics (pp. 146–147). http://hdl.handle.net/20.500.12708/40836 ( reposiTUm)

Todorov, V., Templ, M., & Filzmoser, P. (2009). Robust Outlier Detection in Survey Data. In Abstracts of the International Conference in Robust Statistics (pp. 148–149). http://hdl.handle.net/20.500.12708/40837 ( reposiTUm)

Templ, M., Alfons, A., & Filzmoser, P. (2009). Exploring the multivariate structure of missing values using the R package VIM. In Abstracts of the 5th R useR Conference (p. 194). http://hdl.handle.net/20.500.12708/40839 ( reposiTUm)

Templ, M., Filzmoser, P., & Hron, K. (2009). Missing Values in Compositional Data: Imputation and Diagnostics. In Abstracts of the 6th International Conference on Computational Management Science (p. 16). http://hdl.handle.net/20.500.12708/40840 ( reposiTUm)

Filzmoser, P., Gieber, H., Liebmann, B., & Varmuza, K. (2009). Classification Methods Applied to Chemical Data Using the R Library “chemometrics.” In Conferentia Chemometrica 2009 Abstract Book. Conferentia Chemometrica 2009, Siofok,Ungarn, EU. http://hdl.handle.net/20.500.12708/46571 ( reposiTUm)

Liebmann, B., Filzmoser, P., Friedl, A., & Varmuza, K. (2009). Classical and Robust PLS Regression compared by Repeated Double Cross Validation. In Conferentia Chemometrica 2009 Abstract Book. Conferentia Chemometrica 2009, Siofok,Ungarn, EU. http://hdl.handle.net/20.500.12708/46570 ( reposiTUm)

Varmuza, K., Filzmoser, P., & Liebmann, B. (2009). Random Projection and Projection Pursuit Based PCA Applied to Chemical Data. In Conferentia Chemometrica 2009 Abstract Book. Conferentia Chemometrica 2009, Siofok,Ungarn, EU. http://hdl.handle.net/20.500.12708/46572 ( reposiTUm)

Filzmoser, P., Liebmann, B., & Varmuza, K. (2009). Repeated double cross validation. In 11#^{th} Scandinavian Symposium on Chemometrics SSC11 - Book of Abstracts (p. 39). http://hdl.handle.net/20.500.12708/46415 ( reposiTUm)

Hron, K., Templ, M., & Filzmoser, P. (2008). A Robust Approach for Dealing with Missing Values in Compositional Data. In Abstracts of the International Conference on Robust Statistics 2008 (pp. 48–49). http://hdl.handle.net/20.500.12708/40766 ( reposiTUm)

Wieser, R., Filzmoser, P., Baaske, W. E., & Mader, W. (2008). An Application of Robust Variable Selection with many Variables. In Abstracts of the International Conference On Robust Statistics (p. 115). http://hdl.handle.net/20.500.12708/40771 ( reposiTUm)

Todorov, V., & Filzmoser, P. (2008). An Object Oriented Framework for Robust Multivariate Analysis in R. In Abstracts of the International Conference On Robust Statistics (p. 100). http://hdl.handle.net/20.500.12708/40773 ( reposiTUm)

Filzmoser, P., & Hron, K. (2008). Robust Statistical Methods for Compositional Data. In Abstracts of the International Conference On Robust Statistics (p. 37). http://hdl.handle.net/20.500.12708/40772 ( reposiTUm)

Neykov, N., Filzmoser, P., & Neytchev, P. (2008). Recent developments in robust fitting of mixtures. In Abstracts of the International Conference on Robust Statistics (p. 74). http://hdl.handle.net/20.500.12708/40774 ( reposiTUm)

Todorov, V., & Filzmoser, P. (2008). Robuste multivariate Analyse in R. In Abstracts of the Workshop TU Wien/TU Dresden (p. 27). http://hdl.handle.net/20.500.12708/40779 ( reposiTUm)

Boubela, R., Filzmoser, P., & Piringer, H. (2008). Integration der Statistiksoftware R in die Visualisierungssoftware Bulk Analyzer zur interaktiven Datenanalyse. In Abstracts of the Workshop TU Wien/TU Dresden (p. 8). http://hdl.handle.net/20.500.12708/40781 ( reposiTUm)

Wieser, R., Filzmoser, P., Baaske, W. E., & Mader, W. (2008). Anwendung robuster Variablenselektionsverfahren zur Analyse hochdimensionaler Daten. In Abstracts of the Workshop TU Wien/TU Dresden (p. 32). http://hdl.handle.net/20.500.12708/40782 ( reposiTUm)

Alfons, A., Kraft, S., Templ, M., & Filzmoser, P. (2008). Analyse und Visualisierung von EU-SILC Daten in R. In Abstracts of the Workshop TU Wien/TU Dresden (p. 6). http://hdl.handle.net/20.500.12708/40780 ( reposiTUm)

Hron, K., & Filzmoser, P. (2008). Robuste Biplots für Kompositionsdaten. In Abstracts of the Workshop TU Wien/TU Dresden (p. 16). http://hdl.handle.net/20.500.12708/40778 ( reposiTUm)

Varmuza, K., & Filzmoser, P. (2008). Comparison of some Linear Regression Methods - Available in R - for a QSPR Problem. In 22. CIC- Workshop (p. 96). http://hdl.handle.net/20.500.12708/46315 ( reposiTUm)

Liebminger, A., Seyfang, L., Filzmoser, P., & Varmuza, K. (2007). A new variable selection method based on all subsets regression. In Book of Abstracts. 10#^{th} Scandinavian Symposium on Chemometrics, Lappeenranta, Finnland, EU. http://hdl.handle.net/20.500.12708/45994 ( reposiTUm)

Seyfang, L., Filzmoser, P., & Varmuza, K. (2007). Variable selection by all subsets regression and by genetic algorithms. In Proc.of Conferentia Chemometrica. Conferentia Chemometrica 2007, Budapest, EU. http://hdl.handle.net/20.500.12708/45996 ( reposiTUm)

Filzmoser, P., & Fritz, H. (2007). Exploring High-dimensional Data with Robust Principal Components. In Proceedings of the Eighth International Conference Computer Data Analysis and Modeling (pp. 43–50). Publishing center BSU, Minsk. http://hdl.handle.net/20.500.12708/40706 ( reposiTUm)

Filzmoser, P., Serneels, S., Croux, C., & Van Espen, P. J. (2006). Robust Multivariate Methods: The Projection Pursuit Approach. In M. Spiliopoulou, R. Kruse, C. Borgelt, A. Nürnberger, & W. Gaul (Eds.), From Data and Information Analysis to Knowledge Engineering (pp. 270–277). http://hdl.handle.net/20.500.12708/40615 ( reposiTUm)

Serneels, S., Croux, C., Filzmoser, P., & Van Espen, P. J. (2006). The Partial Robust M-approach. In M. Spiliopoulou, R. Kruse, C. Borgelt, A. Nürnberger, & W. Gaul (Eds.), From Data and Information Analysis to Knowledge Engineering (pp. 230–237). Springer. http://hdl.handle.net/20.500.12708/40616 ( reposiTUm)

Filzmoser, P., Joossens, K., & Croux, C. (2006). Multiple group linear discriminant analysis: robustness and error rate. In A. Rizzi & M. Vichi (Eds.), Compstat 2006, Proceedings in Computational Statistics (pp. 521–532). http://hdl.handle.net/20.500.12708/40617 ( reposiTUm)

Janauer, G., Filzmoser, P., Otahelova, H., Gaberscik, A., Topic, J., Berczik, A., Igic, R., Vulchev, V., Sarbu, A., Kohler, A., & Exler, N. (2006). Macrophyte Habitat Preference, River Restoration, and the WFD: making use of the MIDCC data base. In Proceedings 36th International Conference of IAD (pp. 81–85). Austrian Committee Danube Research/IAD. http://hdl.handle.net/20.500.12708/40640 ( reposiTUm)

Janauer, G., Lanz, E., Filzmoser, P., & Exler, N. (2006). Breg and Brigach, source streams of the Danube: changes based on macrophyte surveys 1967, 1989, and 2004. In Proceedings 36th International Conference of IAD (pp. 86–90). Austrian Committee Danube Research/IAD. http://hdl.handle.net/20.500.12708/40639 ( reposiTUm)

Sarbu, A., Janauer, G., Exler, N., & Filzmoser, P. (2006). The aquatic vegetation of large Danube river branches in Romania. In Proceedings 36th International Conference of IAD (pp. 101–106). Austrian Committee Danube Research/IAD. http://hdl.handle.net/20.500.12708/40638 ( reposiTUm)

Beiträge in Büchern

Filzmoser, P., & Mazak-Huemer, A. (2023). Massive Data Sets – Is Data Quality Still an Issue? In B. Vogel-Heuser & M. Wimmer (Eds.), Digital Transformation (Vol. 1, pp. 269–279). Springer Vieweg. https://doi.org/10.1007/978-3-662-65004-2_11 ( reposiTUm)

Brandl, M., Hauzenberger, C. A., Filzmoser, P., & Trnka, G. (2022). Swieciechow in the south - geochemical provenance of a “flint” axe from Austria. In M. Grygiel & P. Obst (Eds.), Walking among ancient trees. (pp. 533–546). Fundacja Badan Archeologicnych Imenia Profesora Konrada Jazdzewskiego. http://hdl.handle.net/20.500.12708/152234 ( reposiTUm)

Brandl, M., Hauzenberger, C. A., Filzmoser, P., & Martinez, M. M. (2022). Geochemical sourcing of chipped stone tools from Platia Magoula Zarkou. In E. Alram-Stern, K. Gallis, & G. Toufexis (Eds.), Platia Magoula Zarkou. The Neolithic Period. (Vol. 23, pp. 291–309). Austrian Academy of Sciences Press. http://hdl.handle.net/20.500.12708/136503 ( reposiTUm)

Pavlu, I., Filzmoser, P., Menafoglio, A., & Hron, K. (2022). Classification of continuous distributional data using the logratio approach. In P. Brito & S. Dias (Eds.), Analysis of Distributional Data (pp. 184–202). https://doi.org/10.1201/9781315370545-9 ( reposiTUm)

Mumic, N., Leodolter, O., Schwaiger, A., & Filzmoser, P. (2022). Scale Invariant and Robust Pattern Identification in Univariate Time Series, with Application to Growth Trend Detection in Music Streaming Data. In A. Steland & K.-L. Tsui (Eds.), Artificial Intelligence, Big Data and Data Science in Statistics (pp. 25–50). Springer Nature, Cham. https://doi.org/10.1007/978-3-031-07155-3_2 ( reposiTUm)

Templ, M. (2021). Artificial Neural Networks to Impute Rounded Zeros in Compositional Data. In P. Filzmoser, K. Hron, J. A. Martin-Fernandez, & J. Palarea-Albaladejo (Eds.), Advances in Compositional Data Analysis (pp. 163–187). Springer. https://doi.org/10.1007/978-3-030-71175-7_9 ( reposiTUm)

Filzmoser, P., Hron, K., & Menafoglio, A. (2021). Logratio Approach to Distributional Modeling. In A. Daouia & A. Ruiz-Gazen (Eds.), Advances in Contemporary Statistics and Econometrics (pp. 451–470). Springer, Cham. https://doi.org/10.1007/978-3-030-73249-3_23 ( reposiTUm)

Filzmoser, P. (2021). Robust Statistics. In B. S. D. Sagar, Q. Cheng, J. McKinley, & F. Agterberg (Eds.), Encyclopedia of Mathematical Geosciences (pp. 1–5). Springer, Cham. https://doi.org/10.1007/978-3-030-26050-7_425-1 ( reposiTUm)

Rieser, C., & Filzmoser, P. (2021). Outlier detection for pandemic-related data using compositional functional data analysis. In M. del C. Boado-Penas, J. Eisenberg, & S. Şahin (Eds.), Springer Actuarial (pp. 251–266). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-030-78334-1_12 ( reposiTUm)

Monti, G., & Filzmoser, P. (2020). High-dimensional regression with compositional covariates: a robust perspective. In A. Pollice, N. Salvati, & F. Schirripa Spagnolo (Eds.), Book of Short Papers SIS 2020 (pp. 105–110). Pearson. http://hdl.handle.net/20.500.12708/30398 ( reposiTUm)

Filzmoser, P., & Hron, K. (2020). Compositional Data Analysis in Chemometrics. In R. Tauler, B. Walczak, & S. Brown (Eds.), Comprehensive Chemometrics. Chemical and Biochemical Data Analysis (pp. 641–662). Elsevier. https://doi.org/10.1016/B978-0-12-409547-2.14591-3 ( reposiTUm)

Filzmoser, P., Serneels, S., Maronna, R., & Croux, C. (2020). Robust Multivariate Methods in Chemometrics. In S. Brown, R. Tauler, & B. Walczak (Eds.), Comprehensive Chemometrics: Chemical and Biochemical Data Analysis (pp. 393–430). Elsevier. https://doi.org/10.1016/B978-0-12-409547-2.14642-6 ( reposiTUm)

Wurl, A., Falkner, A., Filzmoser, P., Haselböck, A., Mazak, A., & Sperl, S. (2019). A Comprehensive Prediction Approach for Hardware Asset Management. In C. Quix & J. Bernardino (Eds.), Data Management Technologies and Applications (pp. 26–49). Springer Nature Schwitzerland AG 2019. https://doi.org/10.1007/978-3-030-26636-3_2 ( reposiTUm)

Walach, J., Hron, K., & Filzmoser, P. (2018). Data normalization and scaling: Consequences for the analysis in omics sciences. In J. Jaumot, C. Bedia, & R. Tauler (Eds.), Comprehensive Analytical Chemistry. Data Analysis for Omics Sciences: Methods and Applications (pp. 165–196). Elsevier. http://hdl.handle.net/20.500.12708/29784 ( reposiTUm)

Landauer, M., Wurzenberger, M., Skopik, F., Settanni, G., & Filzmoser, P. (2018). Time series analysis: Unsupervised anomaly detection beyond outlier detection. In C. Su & H. Kikuch (Eds.), Information Security Practice and Experience (pp. 16–36). Springer. http://hdl.handle.net/20.500.12708/29783 ( reposiTUm)

Hron, K., & Filzmoser, P. (2015). Exploring compositional data with the robust compositional biplot. In M. Carpita, E. Brentari, & E. M. Qannari (Eds.), Advances in Latent Variables. Part of the series Studies in Theoretical and Applied Statistics (pp. 219–226). Springer International Publishing Switzerland. http://hdl.handle.net/20.500.12708/28836 ( reposiTUm)

Filzmoser, P., & Hron, K. (2015). Robust Coordinates for Compositional Data Using Weighted Balances. In K. Nordhausen & S. Taskinen (Eds.), Modern Nonparametric, Robust and Multivariate Methods (pp. 167–184). Springer International Publishing Switzerland. https://doi.org/10.1007/978-3-319-22404-6_10 ( reposiTUm)

Varmuza, K., & Filzmoser, P. (2015). Chapter 2: Repeated Double Cross Validation (rdCV) - A Strategy for Optimizing Empirical Multivariate Models, and for Comparing Their Prediction Performances. In M. Khanmohammadi (Ed.), Current Applications of Chemometrics (pp. 15–32). Nova Science Publishers. http://hdl.handle.net/20.500.12708/28615 ( reposiTUm)

de la Rosa de Sáa, S., Filzmoser, P., Gil, M. Á., & Lubiano, M. A. (2015). On the Robustness of Absolute Deviations with Fuzzy Data. In Strengthening Links Between Data Analysis and Soft Computing (pp. 133–141). Springer Verlag. https://doi.org/10.1007/978-3-319-10765-3_16 ( reposiTUm)

Ladenberger, A., Uhlbäck, J., Andersson, M., Reimann, C., Tarvainen, T., Morris, G., Sadeghi, M., Eklund, M., & Filzmoser, P. (2014). Elemental patterns in agricultural and grazing land soil in Norway, Finland and Sweden: What have we learned from continental scale mapping. In C. Reimann, M. Birke, A. Demetriades, P. Filzmoser, & P. O´Connor (Eds.), Chemistry of Europe’s Agricultural Soils. Part A: Methodology and Interpretation of the GEMAS Data Set, Geologisches Jahrbuch, Reihe B, Heft 103 (pp. 235–251). E. Schweizerbart’sche Verlagsbuchhandlung oHG. http://hdl.handle.net/20.500.12708/28450 ( reposiTUm)

Filzmoser, P., & Reimann, C. (2014). Multivariate data analysis. In C. Reimann, M. Birke, A. Demetriades, P. Filzmoser, & P. O´Connor (Eds.), Chemistry of Europe’s Agricultural Soils. Part A: Methodology and Interpretation of the GEMAS Data Set, Geologisches Jahrbuch, Reihe B, Heft 102 (pp. 83–92). E. Schweizerbart’sche Verlagsbuchhandlung oHG. http://hdl.handle.net/20.500.12708/28394 ( reposiTUm)

Demetriades, A., Reimann, C., & Filzmoser, P. (2014). Evaluation of GEMAS project quality control results. In C. Reimann, M. Birke, A. Demetriades, P. Filzmoser, & P. O´Connor (Eds.), Chemistry of Europe’s Agricultural Soils. Part A: Methodology and Interpretation of the GEMAS Data Set, Geologisches Jahrbuch, Reihe B, Heft 102 (pp. 47–60). E. Schweizerbart’sche Verlagsbuchhandlung oHG. http://hdl.handle.net/20.500.12708/28383 ( reposiTUm)

Filzmoser, P., Reimann, C., & Birke, M. (2014). Univariate data analysis and mapping. In C. Reimann, M. Birke, A. Demetriades, P. Filzmoser, & P. O´Connor (Eds.), Chemistry of Europe’s Agricultural Soils. Part A: Methodology and Interpretation of the GEMAS Data Set, Geologisches Jahrbuch, Reihe B, Heft 102 (pp. 67–81). E. Schweizerbart’sche Verlagsbuchhandlung oHG. http://hdl.handle.net/20.500.12708/28396 ( reposiTUm)

Hron, K., Filzmoser, P., Templ, M., van den Boogaart, K. G., & Tolosana-Delgado, R. (2014). Robust Regression with Compositional Response: Application to Geosciences. In Mathematics of Planet Earth. Proceedings of the 15th Annual Conference of the International Association for Mathematical Geosciences (pp. 87–90). Springer Verlag Berlin-Heidelberg. https://doi.org/10.1007/978-3-642-32408-6_21 ( reposiTUm)

Hron, K., & Filzmoser, P. (2013). Robust Diagnostics of Fuzzy Clustering Results Using the Compositional Approach. In Synergies of Soft Computing and Statistics for Intelligent Data Analysis (pp. 245–253). Springer Verlag Berlin-Heidelberg. https://doi.org/10.1007/978-3-642-33042-1_27 ( reposiTUm)

Todorov, V., & Filzmoser, P. (2013). Comparing Classical and Robust Sparse PCA. In Synergies of Soft Computing and Statistics for Intelligent Data Analysis (pp. 283–291). Springer Verlag Berlin-Heidelberg. https://doi.org/10.1007/978-3-642-33042-1_31 ( reposiTUm)

Filzmoser, P., & Hron, K. (2013). Robustness for Compositional Data. In C. Becker, R. Fried, & S. Kuhnt (Eds.), Robustness and Complex Data Structures (pp. 117–131). Springer Verlag. https://doi.org/10.1007/978-3-642-35494-6_8 ( reposiTUm)

Reimann, R., Birke, M., & Filzmoser, P. (2011). Data analysis for urban geochemical data. In C. C. Johnson, A. Demetriades, J. Locutura, & R. T. Ottesen (Eds.), Mapping the Chemical Environment of Urban Areas (pp. 99–115). John Wiley & Sons. http://hdl.handle.net/20.500.12708/27266 ( reposiTUm)

Templ, M., Hron, K., & Filzmoser, P. (2011). robCompositions: an R-package for robust statistical analysis of compositional data. In Compositional Data Analysis: Theory and Applications (pp. 341–355). John Wiley & Sons. http://hdl.handle.net/20.500.12708/27252 ( reposiTUm)

Filzmoser, P., & Hron, K. (2011). Robust statistical analysis. In Compositional Data Analysis: Theory and Applications (pp. 59–72). John Wiley & Sons. http://hdl.handle.net/20.500.12708/27253 ( reposiTUm)

Filzmoser, P., Serneels, S., Maronna, R., & Van Espen, P. J. (2009). Robust Multivariate Methods in Chemometrics. In S. D. Brown, R. Tauler, & B. Walczak (Eds.), Comprehensive Chemometrics: Chemical and Biochemical Data Analysis (pp. 663–722). Elsevier. http://hdl.handle.net/20.500.12708/26445 ( reposiTUm)

Baaske, W. E., Filzmoser, P., Mader, W., & Wieser, R. (2009). Agriculture as a success factor for municipalities. In Jahrbuch der Österreichischen Gesellschaft für Agrarökonomie (pp. 21–30). Facultas Verlags- und Buchhandels AG. http://hdl.handle.net/20.500.12708/26439 ( reposiTUm)

Bücher

Filzmoser, P., Hron, K., Martin-Fernandez, J. A., & Palarea-Albaladejo, J. (Eds.). (2021). Advances in Compositional Data Analysis. Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-030-71175-7 ( reposiTUm)

Filzmoser, P., Hron, K., & Templ, M. (2018). Applied Compositional Data Analysis. With Worked Examples in R. Springer Series in Statistics. http://hdl.handle.net/20.500.12708/24457 ( reposiTUm)

Agostinelli, C., Basu, A., Filzmoser, P., & Mukherjee, D. (Eds.). (2016). Recent Advances in Robust Statistics: Theory and Applications. Springer International Publishing. https://doi.org/10.1007/978-81-322-3643-6 ( reposiTUm)

Reimann, C., Birke, M., Demetriades, A., Filzmoser, P., & O´Connor, P. (Eds.). (2014). Part B: General Background Information and Further Analysis of the GEMAS Data Set. E. Schweizerbart’sche Verlagsbuchhandlung oHG. http://hdl.handle.net/20.500.12708/23889 ( reposiTUm)

Reimann, C., Birke, M., Demetriades, A., Filzmoser, P., & O´Connor, P. (Eds.). (2014). Part A: Methodology and Interpretation of the GEMAS Data Set. E. Schweizerbart’sche Verlagsbuchhandlung oHG. http://hdl.handle.net/20.500.12708/23890 ( reposiTUm)

Reimann, C., Birke, M., Demetriades, A., & Filzmoser, P. (Eds.). (2014). Chemistry of Europe’s Agricultural Soils Part A and B (2-volume set), 880 pp | 1 DVD | Hardbound ISBN 978-3-510-96848-0. E. Schweizerbart’sche Verlagsbuchhandlung oHG. http://hdl.handle.net/20.500.12708/23918 ( reposiTUm)

Todorov, V., & Filzmoser, P. (2009). Multivariate Robust Statistics - Methods and Computation. Südwestdeutscher Verlag für Hochschulschriften. http://hdl.handle.net/20.500.12708/22955 ( reposiTUm)

Varmuza, K., & Filzmoser, P. (2009). Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press. http://hdl.handle.net/20.500.12708/22901 ( reposiTUm)

Reimann, C., Filzmoser, P., Garrett, R. G., & Dutter, R. (2008). Statistical Data Analysis Explained. Applied Environmental Statistics with R. John Wiley & Sons. http://hdl.handle.net/20.500.12708/22760 ( reposiTUm)

Fritz, H., & Filzmoser, P. (2008). Plausibility of Databases and the Relation to Imputation Methods. VDM Verlag Dr. Müller. http://hdl.handle.net/20.500.12708/22761 ( reposiTUm)

Dutter, R., Filzmoser, P., & Kharin, Y. (Eds.). (2005). Austrian Journal of Statistics. Österreichische Statistische Gesellschaft. http://hdl.handle.net/20.500.12708/22156 ( reposiTUm)

Tagungsbände

Reimann, C., Filzmoser, P., & Hron, K. (Eds.). (2014). GeoMap Workshop Proceedings. Palacký University, Olomouc, Cz. http://hdl.handle.net/20.500.12708/23888 ( reposiTUm)

Special Issue - 10th International Conference COMPUTER DATA ANALYSIS & MODELLING 2013, Minsk, Belarus ( Vol.43, 3-4). (2014). In P. Filzmoser & M. Templ (Eds.), Austrian Journal of Statistics. Österreichische Statistische Gesellschaft. http://hdl.handle.net/20.500.12708/23891 ( reposiTUm)

Aivazian, S., Filzmoser, P., & Kharin, Y. (Eds.). (2013). Computer Data Analysis and Modeling Theoretical and Applied Stochastics / Proceedings of the Tenth International Conference, Minsk. Publishing center BSU, Minsk. http://hdl.handle.net/20.500.12708/23777 ( reposiTUm)

Hron, K., Filzmoser, P., & Templ, M. (Eds.). (2013). Proceedings of the 5th International Workshop on compositional Data Analysis CoDaWork 2013 June 3-7, 2013, Vorau, Austria. TU WIEN. http://hdl.handle.net/20.500.12708/23721 ( reposiTUm)

Dutter, R., Filzmoser, P., & Kharin, Y. (Eds.). (2008). Special Issue on the Eighth International Conference Computer Data Analysis and Modeling. Österreichische Statistische Gesellschaft. http://hdl.handle.net/20.500.12708/22798 ( reposiTUm)

Aivazian, S., Filzmoser, P., & Kharin, Y. (Eds.). (2004). Computer Data Analysis and Modeling: Robustness and Computer Intensive Methods. Proceedings of the Seventh International Conference, Volume 1. Belarusian State University. http://hdl.handle.net/20.500.12708/22148 ( reposiTUm)

Präsentationen

Filzmoser, P. (2024, October 29). Accounting for spatial dependencies in robust anomaly detection [Presentation]. MinProXT Webinar, Espoo, Finland. ( reposiTUm)

Filzmoser, P. (2024, September 24). Cell-wise robust and sparse principal component analysis [Conference Presentation]. Statistical Modeling with Application, Belgrad, Serbia. ( reposiTUm)

Filzmoser, P. (2024, October 17). Robust regression and classification for high-dimensional compositions [Presentation]. Seminar “Moderne Analytische Chemie“, Wien, Austria. ( reposiTUm)

Filzmoser, P. (2024, September 10). Explainable outlier identification for vector- and matrix-valued observations [Conference Presentation]. AMISTAT - Analytical Methods in Statistics, Bardejov, Slovakia. ( reposiTUm)

Filzmoser, P. (2024, July 30). Cellwise robust and sparse PCA [Conference Presentation]. ICORS meets DSSV 2024, Fairfax, United States of America (the). ( reposiTUm)

Mayrhofer, M., Radojicic, U., & Filzmoser, P. (2024, August 13). Robust covariance estimation for matrix-valued data [Conference Presentation]. Bernoulli-ims 11th World Congress in Probability and Statistics, Bochum, Germany. ( reposiTUm)

Mayrhofer, M., Radojicic, U., & Filzmoser, P. (2024, April 4). Explainable anomaly detection using Shapley values [Conference Presentation]. Statistiktage 2024, Wien, Austria. ( reposiTUm)

Filzmoser, P. (2024, October 7). Outlier detection and explanation for matrix-variate data [Presentation]. Seminar of the Statistics Department Aveiro, Aveiro, Portugal. ( reposiTUm)

Filzmoser, P. (2024, October 25). Principal Component Analysis: Extensions towards robustness and sparsity [Conference Presentation]. MATTEX 2024, Shumen, Bulgaria. ( reposiTUm)

Radojicic, U., Mayrhofer, M., & Filzmoser, P. (2024, December 17). Expainable outlier detection for multivariate random processes with separable covariance structure [Conference Presentation]. ICSDS2024, Nizza, France. http://hdl.handle.net/20.500.12708/210712 ( reposiTUm)

Radojicic, U., Mayrhofer, M., & Filzmoser, P. (2024, October 3). Explainable outlier detection for multivariate functional data based on a functional Mahalanobis distance [Presentation]. Statistical seminar of Department of Mathematics, Croatia. http://hdl.handle.net/20.500.12708/210710 ( reposiTUm)

Radojičić, U., Mayrhofer, M., & Filzmoser, P. (2024, October 25). Explainable Outlier Detection for Multivariate Functional Data [Presentation]. Turku Applied Mathematics and Statistics Seminar, Finland. http://hdl.handle.net/20.500.12708/210711 ( reposiTUm)

Mayrhofer, M., Radojičić, U., & Filzmoser, P. (2024, July 31). Robust PCA and explainable outlier detection for multivariate functional data based on a functional Mahalanobis distance [Conference Presentation]. ICORS meets DSSV 2024, United States of America (the). http://hdl.handle.net/20.500.12708/210706 ( reposiTUm)

Oguamalam, J., Radojičić, U., & Filzmoser, P. (2024, September 5). Functional Outlier Detection [Conference Presentation]. SMPS2024, Salzburg, Austria. http://hdl.handle.net/20.500.12708/210707 ( reposiTUm)

Filzmoser, P., Eisenberg, J., Daniilidis, A., & Toninelli, F. L. (2024, January 17). Institute of Statistics and Mathematical Methods in Economics [Presentation]. Präsentation an der Fakultät, Wien, Austria. ( reposiTUm)

Puchhammer, P., Wilms, I., & Filzmoser, P. (2024, July 29). Robust sparse PCA for spatial data [Conference Presentation]. ICORS meets DSSV 2024, Fairfax, United States of America (the). http://hdl.handle.net/20.500.12708/200069 ( reposiTUm)

Puchhammer, P., Filzmoser, P., & Wilms, I. (2024, April 4). Groupwise sparse PCA for spatial data [Conference Presentation]. Österreichische Statistiktage 2024 (2024, Wien), Wien, Austria. http://hdl.handle.net/20.500.12708/200066 ( reposiTUm)

Filzmoser, P. (2023, April 18). Robust Sparse Multinomial Regression [Presentation]. DaSSWeb -- Data Science and Statistics Webinar, Portugal. ( reposiTUm)

Puchhammer, P., & Filzmoser, P. (2023, August 7). Spatial outlier detection using the spatially smoothed MRCD [Conference Presentation]. 22nd Annual Conference of the International Association for Mathematical Geosciences 2023 (IAMG2023), Trondheim, Norway. ( reposiTUm)

Filzmoser, P. (2023, April 13). Robust statistical methods applied to high-dimensional data from tribology [Conference Presentation]. ANAKON 2023, Wien, Austria. ( reposiTUm)

Neubauer, L., & Filzmoser, P. (2023, March 7). Improving Forecasts for Time Series of Different Lengths by “Averaging”, with Application to Food Demand Prediction [Conference Presentation]. 16th German Probability and Statistics Days Essen 2023, Essen, Germany. ( reposiTUm)

Piccolotto, N., Bögl, M., Mühlmann, C., Nordhausen, K., Filzmoser, P., Schmidt, J., & Miksch, S. (2023, October 26). Data Type Agnostic Visual Sensitivity Analysis [Conference Presentation]. IEEE VIS 2023, Melbourne, Australia. http://hdl.handle.net/20.500.12708/189927 ( reposiTUm)

Filzmoser, P., & Monti, G. S. (2023, June 12). A robust knockoff filter for sparse regression with microbiome compositions [Conference Presentation]. ODAM 2023, Olomouc, Czechia. ( reposiTUm)

Puchhammer, P., & Filzmoser, P. (2023, June 12). Detecting Local Outliers Using the Spatially Smoothed MRCD Estimator [Conference Presentation]. Olomoucian Days of Applied Mathematics ODAM 2023, Olomouc, Czechia. ( reposiTUm)

Parzer, R., Vana Gür, L., & Filzmoser, P. (2023, March 8). High-dimensional Regression using Screening, Random Projection and Averaging [Conference Presentation]. 16th German Probability and Statistics Days 2023, Essen, Germany. ( reposiTUm)

Mayrhofer, M., Radojicic, U., Lewitschnig, H., & Filzmoser, P. (2023, May 24). Outlier detection and explanation for matrix-valued data [Conference Presentation]. International Conference on Robust Statistics (ICORS) - 2023, Toulouse, France. http://hdl.handle.net/20.500.12708/192117 ( reposiTUm)

Mayrhofer, M., Lewitschnig, H., & Filzmoser, P. (2023, October 19). New Mission Profile Model Using Functional Data Analysis [Poster Presentation]. Infineon meets University 2023, Germany. http://hdl.handle.net/20.500.12708/192440 ( reposiTUm)

Pfeiffer, P., Alfons, A., & Filzmoser, P. (2023, May 24). Robust and Sparse CCA: An Algorithm for Dimension Reduction via Sparsity Inducing Penalties. [Conference Presentation]. International Conference on Robust Statistics (ICORS 2023), Toulouse, France. http://hdl.handle.net/20.500.12708/192266 ( reposiTUm)

Filzmoser, P. (2022, September 20). Robust linear and logistic regression for high-dimensional compositional data [Conference Presentation]. Applied Statistics 2022, Ljubljana, Slovenia. ( reposiTUm)

Mayrhofer, M., & Filzmoser, P. (2022, July 6). Outlier explanation using Shapley values and Mahalanobis distances [Conference Presentation]. International Conference on Robust Statistics (ICORS 2022), Waterloo, Canada. ( reposiTUm)

Filzmoser, P. (2022, April 7). Statistik basierend auf absoluter bzw. relativer Information [Conference Presentation]. TUforMath, Wien, Austria. http://hdl.handle.net/20.500.12708/153181 ( reposiTUm)

Piccolotto, N., Bögl, M., Mühlmann, C., Nordhausen, K., Filzmoser, P., & Miksch, S. (2022, June 15). Visual Parameter Selection for Spatial Blind Source Separation [Conference Presentation]. EuroVis 2022, Rome, Italy. ( reposiTUm)

Filzmoser, P. (2022, August 31). Benford goes multivariate: A new fraud detection method, with application to music streaming data [Conference Presentation]. Models and Learning in Clustering and Classification, Catania, Italy. ( reposiTUm)

Filzmoser, P. (2022, June 7). Outliers and compositional data [Presentation]. SEMACRET kick-off meeting, Finland. ( reposiTUm)

Filzmoser, P. (2022, July 5). Robust multinomial regression in high dimensions [Conference Presentation]. International Conference on Robust Statistics (ICORS 2022), Waterloo, Canada. ( reposiTUm)

Filzmoser, P. (2022, May 31). Robust and sparse multinomial regression [Presentation]. Statistics and Econometrics Seminar, Berlin, Germany. ( reposiTUm)

Pfeiffer, P., Alfons, A., & Filzmoser, P. (2022, August 24). Efficient computation of robust multivariate maximum association [Conference Presentation]. 24th International Conference on Computational Statistics, Bologna, Italy. http://hdl.handle.net/20.500.12708/152955 ( reposiTUm)

Pfeiffer, P., Ronai, B., Vorlaufer, G., Dörr, N., & Filzmoser, P. (2022, September 15). Prediction of engine oil degradation based on FTIR spectroscopic data [Conference Presentation]. Symposium 2022 der Österreichischen Tribologischen Gesellschaft (ÖTG), Wr. Neustadt, Austria. http://hdl.handle.net/20.500.12708/152959 ( reposiTUm)

Pfeiffer, P., Ronai, B., Vorlaufer, G., Dörr, N., & Filzmoser, P. (2022, April 28). Weighted LASSO feature selection for the analysis of FT-IR spectra applied to relate engine oil degradation patterns [Conference Presentation]. Tribology International Conference 2022, Barcelona, Spain. http://hdl.handle.net/20.500.12708/152957 ( reposiTUm)

Pfeiffer, P., Ronai, B., Vorlaufer, G., Dörr, N., & Filzmoser, P. (2022, June 21). Prediction of engine oil degradation based on FTIR spectra and weighted LASSO regression [Conference Presentation]. 5th Young Tribological Researcher Symposium (YTRS), Karlsruhe, Germany. http://hdl.handle.net/20.500.12708/152958 ( reposiTUm)

Filzmoser, P. (2021). A robust method to classify high-dimensional microbiome compositions. 63rd Session of the International Statistical Institute, Den Haag, Netherlands (the). http://hdl.handle.net/20.500.12708/123382 ( reposiTUm)

Filzmoser, P. (2021). Robustness aspects for the statistical analysis related to industrial applications. International Conference on Mathematical Methods in Economy and Industry (MMEI), Smolenice, Slovakia. http://hdl.handle.net/20.500.12708/123384 ( reposiTUm)

Filzmoser, P. (2021). Relativ versus absolut: Eine Einführung in die Analyse von Kompositionsdaten. AC2T Student Camp, Vorau, Austria. http://hdl.handle.net/20.500.12708/123385 ( reposiTUm)

Filzmoser, P. (2021). Garbage in - garbage out: Die Auswirkungen der Datenqualität auf Machine Learning. Zukunftsfragen des Baubetriebes, Wien, Austria. http://hdl.handle.net/20.500.12708/123369 ( reposiTUm)

Filzmoser, P. (2021). Introduction to data analysis techniques and the CODA approach. Short course on Fingerprinting techniques in mineral exploration, Norwegen, Norway. http://hdl.handle.net/20.500.12708/123352 ( reposiTUm)

Filzmoser, P. (2021). Robust logistic zero-sum regression for compositional data. Online Conference Data Science, Statistics & Visualization (DSSV) 2021, Rotterdam, Netherlands (the). http://hdl.handle.net/20.500.12708/123330 ( reposiTUm)

Filzmoser, P. (2021). Introduction to robust statistics. Data Science Group of VNR Verlag, Deutschland, Germany. http://hdl.handle.net/20.500.12708/123353 ( reposiTUm)

Rieser, C., & Filzmoser, P. (2021). Multivariate functional outlier detection for compositions. Online Conference Data Science, Statistics & Visualization (DSSV) 2021, Rotterdam, Netherlands (the). http://hdl.handle.net/20.500.12708/123473 ( reposiTUm)

Filzmoser, P., Lubbe, S., & Templ, M. (2020). Strategies to replace high proportions of zeros in compositional data. Online Conference - 1st Conference on Information Technology and Data Science, Debrecen, Hungary. http://hdl.handle.net/20.500.12708/123104 ( reposiTUm)

Filzmoser, P. (2020). Statistical data analysis of surface geochemical data including case studies from Finland, Greenland and France. Online-Konferenz, Abschluss-Meeting UpDeep, Espoo, Finland. http://hdl.handle.net/20.500.12708/123092 ( reposiTUm)

Varmuza, K., Rados, E., Herzig, C., Limbeck, A., Pittenauer, E., Allmaier, G., & Filzmoser, P. (2020). ICP-MS of meteorite samples: Chemometric evaluation of diversity and discrimination. 31st Mass Spec Forum Vienna, Wien, Austria. http://hdl.handle.net/20.500.12708/123142 ( reposiTUm)

Mühlmann, C., Filzmoser, P., & Nordhausen, K. (2020). Local Difference Matrices for Spatial Blind Source Separation. 3rd Conference of the Arabian Journal of Geosciences (CAJG), Tunisia. http://hdl.handle.net/20.500.12708/123225 ( reposiTUm)

Rieser, C., & Filzmoser, P. (2020). Compositional Trend Filtering. Online Conference - 1st Conference on Information Technology and Data Science, Debrecen, Hungary. http://hdl.handle.net/20.500.12708/123474 ( reposiTUm)

Filzmoser, P., Brodinova, S., Ortner, T., Breiteneder, C., & Rohm, M. (2020). Robust and sparse k-means clustering in high dimension. Seminarvortrag an der JKU Linz, Linz, Austria. http://hdl.handle.net/20.500.12708/123091 ( reposiTUm)

Varmuza, K., Filzmoser, P., Hilchenbach, M., Kissel, J., Stenzel, O., Merouane, S., & Paquette, J. (2019). Cometary particle surfaces - characterized by chemometric evaluation of secondary ion mass spectra. FKA20, Conference on Solid State Analysis, 20. Tagung Festkörperanalytik, Wien, Austria. http://hdl.handle.net/20.500.12708/122919 ( reposiTUm)

Filzmoser, P. (2019). Correlation analysis for compositional environmental data. Deutsche Statistische Woche, Konstanz, Austria. http://hdl.handle.net/20.500.12708/122878 ( reposiTUm)

Filzmoser, P. (2019). k-means clustering for high-dimensional data: a robust and sparse method. ÖSG Statistiktage, Wien, Austria. http://hdl.handle.net/20.500.12708/122881 ( reposiTUm)

Filzmoser, P. (2019). Correlation analysis for compositional data. Meet The Jury Seminar, Leuven, Belgium. http://hdl.handle.net/20.500.12708/122882 ( reposiTUm)

Filzmoser, P. (2019). Buzzword Data Science -- an Overview of Common Methods and their Use in R. Annual Meeting of the Austrian Actuarial Association, Wien, Austria. http://hdl.handle.net/20.500.12708/122883 ( reposiTUm)

Filzmoser, P. (2019). Robust regression and classification methods for high-dimensional data. Predictive Analytics Konferenz, Wien, Austria. http://hdl.handle.net/20.500.12708/122880 ( reposiTUm)

Filzmoser, P. (2019). Robust and sparse k-means clustering for high.dimensional data. CDAM 2019, Minsk, Belarus. http://hdl.handle.net/20.500.12708/122879 ( reposiTUm)

Varmuza, K., Filzmoser, P., Fray, N., Cottin, H., Merouane, S., Stenzel, O., & Kissel, J. (2019). Composition of cometary particles versus distance to sun during sample collection - based on multivariate evaluation of mass spectral data (Rosetta/COSIMA). Conferentia Chemometrica 2019, Karcag, Hungary. http://hdl.handle.net/20.500.12708/122935 ( reposiTUm)

Rieser, C., Miksova, D., & Filzmoser, P. (2019). Detection of mineralization using the second derivative of log‐ratios. CoDaWork 2019, Terrassa, Spain. http://hdl.handle.net/20.500.12708/122979 ( reposiTUm)

Miksova, D., Rieser, C., & Filzmoser, P. (2019). Identification of mineralization in geochemistry based on the spatial curvature of log-ratio. Olomoucian Days of Applied Mathematics (ODAM 2019), Olomouc, Czechia. http://hdl.handle.net/20.500.12708/123038 ( reposiTUm)

Miksova, D., Rieser, C., & Filzmoser, P. (2019). Detection of mineralization using the curvature of log‐ratios. CoDaWork 2019, Terrassa, Spain. http://hdl.handle.net/20.500.12708/123039 ( reposiTUm)

Filzmoser, P. (2019). Robust and sparse estimation methods for linear and logistic regression in high dimensions. DMS-2019, Van, Turkey. http://hdl.handle.net/20.500.12708/122862 ( reposiTUm)

Filzmoser, P. (2019). Outlier detection in compositional data: from row-wise to cell-wise. ISI 2019, Kuala Lumpur, Malaysia. http://hdl.handle.net/20.500.12708/122866 ( reposiTUm)

Filzmoser, P. (2019). Linear methods for regression and classification. Data Science School, University of Bolzano, Bozen, Italy. http://hdl.handle.net/20.500.12708/122867 ( reposiTUm)

Filzmoser, P., Mumic, N., & Kostadinova, R. (2019). Fraud detection in the digital music industry. Benford’s Law Conference, Stresa, Italy. http://hdl.handle.net/20.500.12708/122863 ( reposiTUm)

Filzmoser, P. (2019). Outliers and compositional data. IAMG2019, Pennsylvania, United States of America (the). http://hdl.handle.net/20.500.12708/122865 ( reposiTUm)

Filzmoser, P. (2019). Advanced methods of classification and regression. ÖAW AI Summer School 2019, Ligist, Austria. http://hdl.handle.net/20.500.12708/122864 ( reposiTUm)

Filzmoser, P. (2019). The log-ratio approach to handle relative information. JOCLAD 2019, Viseu, Portugal. http://hdl.handle.net/20.500.12708/122850 ( reposiTUm)

Filzmoser, P. (2019). Compositional Data Analysis. JOCLAD 2019, Viseu, Portugal. http://hdl.handle.net/20.500.12708/122849 ( reposiTUm)

Filzmoser, P., Brodinova, S., Ortner, T., Breiteneder, C., & Rohm, M. (2019). Robust k-means-based clustering for high-dimensional data. International Conference on Robust Statistics (ICORS 2019), Guayaquil, Ecuador. http://hdl.handle.net/20.500.12708/122853 ( reposiTUm)

Rieser, C., & Filzmoser, P. (2019). Piecewise smoothing splines. ÖSG Statistiktage, Wien, Austria. http://hdl.handle.net/20.500.12708/122977 ( reposiTUm)

Filzmoser, P. (2019). Potentials of compositional data analysis in practical applications. CARME 2019, Stellenbosch, South Africa. http://hdl.handle.net/20.500.12708/122836 ( reposiTUm)

Filzmoser, P. (2019). Robust and sparse classification in high dimensions. Seminar presentation, University of Stellenbosch, Institute of Statistics, South Africa. http://hdl.handle.net/20.500.12708/122837 ( reposiTUm)

Varmuza, K., Filzmoser, P., Ortner, T., Hilchenbach, M., Kissel, J., Merouane, S., & Cottin, H. (2019). One-class classification for the recognition of relevant measurements - applied to mass spectra from cometary and meteoritic particles. 16th Scandinavian Symposium on Chemometrics (SSC16), Nesbru / Oslo, Norway. http://hdl.handle.net/20.500.12708/122918 ( reposiTUm)

Crocetti, L., Dorigo, W., Martens, B., Filzmoser, P., & Fernandez-Prieto, D. (2019). Impacts of Climatic Oscillations on Mediterranean Hydrology. ESA Living Planet Symposium 2019, Milan, Italy. http://hdl.handle.net/20.500.12708/83793 ( reposiTUm)

Miksova, D., & Filzmoser, P. (2018). Estimation values above an upper detection limit in compositional data. Data Science, Statistics & Visualization (DSSV) 2018, Wien, Austria. http://hdl.handle.net/20.500.12708/122627 ( reposiTUm)

Walach, J., Filzmoser, P., & Hron, K. (2018). The use of log-ratio methodology in cell-wise diagnostic. Data Science, Statistics & Visualization (DSSV) 2018, Wien, Austria. http://hdl.handle.net/20.500.12708/122633 ( reposiTUm)

Varmuza, K., Filzmoser, P., Hoffmann, I., Hilchenbach, M., Kissel, J., Merouane, S., Paquette, J., & Stenzel, O. (2018). Mass spectrometry near comet 67P (Rosetta/COSIMA). 29th Mass Spec Forum, Wien, Austria. http://hdl.handle.net/20.500.12708/122572 ( reposiTUm)

Filzmoser, P. (2018). The log-ratio methodology for compositional data analysis: concepts and applications. Statistische Woche 2018, Linz, Austria. http://hdl.handle.net/20.500.12708/122562 ( reposiTUm)

Filzmoser, P. (2018). Robust and sparse estimation methods for linear and logistic regression in high dimensions. Seminar at the Department of Finance, Accounting and Statistics, Wirtschaftsuniversitaet Wien, WU Wien, Austria. http://hdl.handle.net/20.500.12708/122557 ( reposiTUm)

Filzmoser, P. (2018). Robust linear and logistic regression in high dimension. Seminar at the Center for Medical Statistics, Informatics, and Intelligent Systems, MedUni Wien, Austria. http://hdl.handle.net/20.500.12708/122558 ( reposiTUm)

Filzmoser, P. (2018). Robust estimators of maximum association. ISOR Colloquium at University of Vienna, University of Vienna, Austria. http://hdl.handle.net/20.500.12708/122560 ( reposiTUm)

Filzmoser, P. (2018). What statistical tool is best? Drawing Insights from Complex Data, TU Wien, Austria. http://hdl.handle.net/20.500.12708/122554 ( reposiTUm)

Miksova, D., & Filzmoser, P. (2018). Replacement of values above an upper detection limit in compositions. IAMG 2018, Olomouc, Czechia. http://hdl.handle.net/20.500.12708/122643 ( reposiTUm)

Filzmoser, P. (2018). The log-ratio methodology: Major concepts, robustness, and practical use. International Conference on Computational Statistics (COMPSTAT 2018), Iasi, Romania. http://hdl.handle.net/20.500.12708/122561 ( reposiTUm)

Filzmoser, P. (2018). Robust maximum association estimators. International Conference on Robust Statistics (ICORS 2018), Leuven, Belgium. http://hdl.handle.net/20.500.12708/122559 ( reposiTUm)

Filzmoser, P. (2018). Choice of influencing factors: Lasso. BASF: Data Science Compact Course, Grossraeschen, Germany. http://hdl.handle.net/20.500.12708/122552 ( reposiTUm)

Filzmoser, P. (2018). Robust elastic net (logistic) regression for high dimensional data. Visual Data Science and its role in Computational Medicine, Delft, Netherlands (the). http://hdl.handle.net/20.500.12708/122553 ( reposiTUm)

Filzmoser, P. (2018). Estimators for robust maximum association. Modern Stochastics: Theory and Applications, Kiew, Ukraine. http://hdl.handle.net/20.500.12708/122556 ( reposiTUm)

Filzmoser, P. (2018). Robust maximum association estimators. Forecasting from Complexity, Minneapolis, United States of America (the). http://hdl.handle.net/20.500.12708/122555 ( reposiTUm)

Walach, J., Filzmoser, P., Hron, K., & Kouril, S. (2018). Cell-wise outlier diagnostics based on pairwise log-ratios. Chemometrics in Analytical Chemistry Conference (CAC), Halifax, Canada. http://hdl.handle.net/20.500.12708/122636 ( reposiTUm)

Kurnaz, F. S., Hoffmann, I., & Filzmoser, P. (2017). Robust and sparse methods for high-dimensional linear and logistic regression. MOVISS - Metabolomic Bio & Data 2017, Vorau, Austria. http://hdl.handle.net/20.500.12708/122017 ( reposiTUm)

Walach, J., Filzmoser, P., Hron, K., Walczak, B., & Najdekr, L. (2017). Cell-wise outlier diagnostics and its use for biomarker identification. MOVISS - Metabolomic Bio & Data 2017, Vorau, Austria. http://hdl.handle.net/20.500.12708/122021 ( reposiTUm)

Brodinova, S., Filzmoser, P., Ortner, T., Breiteneder, C., & Zaharieva, M. (2017). Finding groups in large and high-dimensional data using a k-means-based algorithm. MOVISS - Metabolomic Bio & Data 2017, Vorau, Austria. http://hdl.handle.net/20.500.12708/122033 ( reposiTUm)

Bögl, M., Filzmoser, P., Gschwandtner, T., Lammarsch, T., Leite, R. A., Miksch, S., & Rind, A. (2017). Cycle Plot Revisited: Multivariate Outlier Detection Using a Distance-Based Abstraction. Eurographics / IEEE VGTC Conference on Visualization (EuroVis 2017), Barcelona, Spain. http://hdl.handle.net/20.500.12708/86509 ( reposiTUm)

Ortner, T., Filzmoser, P., Brodinova, S., Zaharieva, M., & Breiteneder, C. (2017). Local projection for outlier detection. Olomouc Days of Applied Mathematics (ODAM 2017), Olomouc, Czechia. http://hdl.handle.net/20.500.12708/122019 ( reposiTUm)

Walach, J., Filzmoser, P., Hron, K., Walczak, B., & Najdekr, L. (2017). A robust pairwise log-ratio approach for variable selection and cell-wise outlier diagnostics with focus on metabolomic data. CoDaWork 2017, Abbadia San Salvatore, Italy. http://hdl.handle.net/20.500.12708/122031 ( reposiTUm)

Filzmoser, P. (2017). Symmetric coordinates for determining pairwise association between compositional parts. CoDaWork 2017, Abbadia San Salvatore, Italy. http://hdl.handle.net/20.500.12708/122028 ( reposiTUm)

Varmuza, K., Filzmoser, P., Hoffmann, I., Walach, J., Cottin, H., Fray, N., Briois, C., Silén, J., Stenzel, O., Kissel, J., & Hilchenbach, M. (2017). Significance of variables for discrimination - applied to the search of organic ions in mass spectra measured on cometary particles. Conferentia Chemometrica 2017, Gyöngyös-Farkasmály, Hungary. http://hdl.handle.net/20.500.12708/122041 ( reposiTUm)

Filzmoser, P. (2017). Robust and sparse estimation methods for high dimensional linear and logistic regression. Workshop devoted to the 60th birthday of Peter Rousseeuw, Leuven, Belgium. http://hdl.handle.net/20.500.12708/122043 ( reposiTUm)

Filzmoser, P. (2017). Compositional data analysis of geochemical data. Kick-off Project Meeting, Espoo, Finland. http://hdl.handle.net/20.500.12708/122045 ( reposiTUm)

Templ, M., Hron, K., & Filzmoser, P. (2017). Exploring outliers in compositional data with structural zeros. CoDaWork 2017, Abbadia San Salvatore, Italy. http://hdl.handle.net/20.500.12708/121859 ( reposiTUm)

Hoffmann, I., Filzmoser, P., & Serneels, S. (2017). Inference for sparse and robust partial least squares regression. Olomouc Days of Applied Mathematics (ODAM 2017), Olomouc, Czechia. http://hdl.handle.net/20.500.12708/122018 ( reposiTUm)

Walach, J., Filzmoser, P., Hron, K., Walczak, B., & Najdekr, L. (2017). Variable selection method based on a pairwise log-ratio approach and cell-wise outlier diagnostics. Olomouc Days of Applied Mathematics (ODAM 2017), Olomouc, Czechia. http://hdl.handle.net/20.500.12708/122020 ( reposiTUm)

Walach, J., Filzmoser, P., Hron, K., Walczak, B., & Najdekr, L. (2017). A new method for variable selection in a two and multi-group case. ERCIM 2017 - International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2017), London, United Kingdom of Great Britain and Northern Ireland (the). http://hdl.handle.net/20.500.12708/122022 ( reposiTUm)

Tobin, J., Walach, J., Beer, D. de, Williams, P., Filzmoser, P., & Walczak, B. (2017). Untargeted analysis of fermentation process of rooibos tea samples. SCC15 2017 - 15th Scandinavian Symposium on Chemometrics, Naantali, Finland. http://hdl.handle.net/20.500.12708/122023 ( reposiTUm)

Filzmoser, P. (2017). PLS for regression and binary classification: robustness and sparsity. 7th International Chemometrics Research Meeting (ICRM 2017), Berg en Dal, Netherlands (the). http://hdl.handle.net/20.500.12708/122027 ( reposiTUm)

Varmuza, K., Brandstätter, F., Cottin, H., Engrand, C., Ferrière, L., Filzmoser, P., Fray, N., Hilchenbach, M., Hoffmann, I., Kissel, J., Koeberl, C., Modica, P., Paquette, J., & Stenzel, O. (2017). Comet and meteorite particle surface characterization by multi-variate data analyses using TOF‐SIMS data from COSIMA/Rosetta. ANAKON 2017, Gesellschaft Deutscher Chemiker, Tubingen, Germany. http://hdl.handle.net/20.500.12708/122038 ( reposiTUm)

Varmuza, K., Baklouti, D., Bardyn, A., Cottin, H., Engrand, C., Filzmoser, P., Fray, N., Hilchenbach, M., Hoffmann, I., Kissel, J., Modica, P., Silén, J., Siljeström, S., & Stenzel, O. (2017). Comet dust composition explored by chemometric methods using mass spectral data from COSIMA/ROSETTA. 15th Scandinavian Symposium on Chemometrics (SSC15), Naantali, Finland. http://hdl.handle.net/20.500.12708/122040 ( reposiTUm)

Brodinova, S., Filzmoser, P., Ortner, T., Zaharieva, M., & Breiteneder, C. (2017). Grouping and outlier detection using robust sparse clustering. Olomouc Days of Applied Mathematics (ODAM 2017), Olomouc, Czechia. http://hdl.handle.net/20.500.12708/122037 ( reposiTUm)

Kurnaz, F. S., Hoffmann, I., & Filzmoser, P. (2017). Robust and sparse estimation methods for high dimensional linear and logistic regression. ECDA 2017 - IVth European Conference on Data Analysis 2017, Wroclaw, Poland. http://hdl.handle.net/20.500.12708/122032 ( reposiTUm)

Hoffmann, I., Brandstätter, F., Engrand, C., Ferrière, L., Filzmoser, P., Hilchenbach, M., Koeberl, C., & Varmuza, K. (2016). Meteorite classification by TOF-SIMS-chemometrics. 27th Mass Spec Forum Vienna, Wien, Austria. http://hdl.handle.net/20.500.12708/121567 ( reposiTUm)

Filzmoser, P. (2016). Robust statistics: Theory and practice. Summer School System Simulation Computational Complex Systems, Bad Fischau, Austria. http://hdl.handle.net/20.500.12708/121529 ( reposiTUm)

Filzmoser, P., Hoffmann, I., Serneels, S., Croux, C., & Varmuza, K. (2016). Sparse and robust PLS for regression and binary classification. International Conference on Computational Statistics (Compstat 2016), Oviedo, Spain. http://hdl.handle.net/20.500.12708/121531 ( reposiTUm)

Glock, B., Endel, F., Endel, G., Popper, N., Sandholzer, K., Rinner, C., Duftschmid, G., Holl, J., Wagner-Pinter, M., Mert, M. C., & Filzmoser, P. (2016). How sick is Austria? A decision support framework for different evaluations of the burden of disease within the Austrian population based in different data sources. International Population Data Linkage Conference 2016, Swansea, United Kingdom of Great Britain and Northern Ireland (the). http://hdl.handle.net/20.500.12708/121539 ( reposiTUm)

Walach, J., Filzmoser, P., Hron, K., & Walczak, B. (2016). A Pairwise Log-Ratio Method For The Identification Of Biomarkers. Xvi Chemometrics In Analytical Chemistry, Barcelona, Spain. http://hdl.handle.net/20.500.12708/121541 ( reposiTUm)

Hron, K., Filzmoser, P., & Gardlo, A. (2016). Univariate analysis of compositional data using weighted balances. International Conference on Computational Statistics (Compstat 2016), Oviedo, Spain. http://hdl.handle.net/20.500.12708/121540 ( reposiTUm)

Ortner, T., Filzmoser, P., Zaharieva, M., Breiteneder, C., & Brodinova, S. (2016). Guided projections for analysising the structure of high dimensional data. International Conference of the ERCIM WG on Computational and Methodological Statistics, Sevilla, Spain. http://hdl.handle.net/20.500.12708/121546 ( reposiTUm)

Hoffmann, I., Filzmoser, P., & Croux, C. (2016). Robust and sparse classification by the optimal scoring approach. International Conference of the ERCIM WG on Computational and Methodological Statistics, Sevilla, Spain. http://hdl.handle.net/20.500.12708/121545 ( reposiTUm)

Hoffmann, I., Filzmoser, P., & Croux, C. (2016). Robust and sparse multiclass classification by the optimal scoring approach. International Conference on Robust Statistics, Parma, Italy. http://hdl.handle.net/20.500.12708/121544 ( reposiTUm)

Hron, K., Templ, M., & Filzmoser, P. (2016). Exploring outliers in compositional data with structural zeros. International Conference of the ERCIM WG on Computational and Methodological Statistics, Seville, Spain. http://hdl.handle.net/20.500.12708/121857 ( reposiTUm)

Filzmoser, P. (2016). Robustness in practice. International Conference of Robust Statistics (ICORS 2016), Genf, Switzerland. http://hdl.handle.net/20.500.12708/121530 ( reposiTUm)

Hoffmann, I., Filzmoser, P., & Croux, C. (2016). Robust and Sparse Multiclass Classification by the Optimal Scoring Approach. International Conference COMPUTER DATA ANALYSIS & MODELING, Minsk, Belarus. http://hdl.handle.net/20.500.12708/121542 ( reposiTUm)

Walach, J., Filzmoser, P., Hron, K., & Walczak, B. (2016). A Pairwise Log-Ratio Method for the Identification Biomarkers. International Conference COMPUTER DATA ANALYSIS & MODELING, Minsk, Belarus. http://hdl.handle.net/20.500.12708/121543 ( reposiTUm)

Brito, P., Duarte Silva, P., & Filzmoser, P. (2016). Robust multivariate analysis of interval data. SINAPE - Simpósio Nacional de Probabilidade e Estatística - 2016, Porto Alegre, Brazil. http://hdl.handle.net/20.500.12708/121538 ( reposiTUm)

Todorov, V., Hruzova, K., Hron, K., & Filzmoser, P. (2016). An R package for robust orthogonal regression for compositional data. 61^a Reunião Anual da Região Brasileira da Sociedade Internacional de Biometria (RBras 2016), El Salvador. http://hdl.handle.net/20.500.12708/121441 ( reposiTUm)

Todorov, V., Hruzova, K., Hron, K., & Filzmoser, P. (2016). Robust orthogonal regression for compositional data in R. International Conference of Robust Statistics (ICORS 2016), Genf, Switzerland. http://hdl.handle.net/20.500.12708/121442 ( reposiTUm)

Brodinova, S., Zaharieva, M., Filzmoser, P., Ortner, T., & Breiteneder, C. (2016). Group Detection in the Context of Imbalanced Data. International Conference COMPUTER DATA ANALYSIS & MODELING, Minsk, Belarus. http://hdl.handle.net/20.500.12708/86323 ( reposiTUm)

Ortner, T., Filzmoser, P., Brodinova, S., Zaharieva, M., & Breiteneder, C. (2016). Forward Projection for High-Dimensional Data. International Conference COMPUTER DATA ANALYSIS & MODELING, Minsk, Belarus. http://hdl.handle.net/20.500.12708/86324 ( reposiTUm)

Gussenbauer, J., Filzmoser, P., Templ, M., & Dupriez, O. (2015). Robust Statistical Methods for Outlier Detection with Application to Household Expenditure Data. Statistiktage 2015, Wien, Austria. http://hdl.handle.net/20.500.12708/121182 ( reposiTUm)

Templ, M., Gussenbauer, J., Filzmoser, P., & Dupriez, O. (2015). Outlier detection in complex survey data including semi-continuous components and missing values. ERCIM 2015, London, United Kingdom of Great Britain and Northern Ireland (the). http://hdl.handle.net/20.500.12708/121233 ( reposiTUm)

Filzmoser, P. (2015). Local multivariate outlier identification. Universität Olomouc, Olomouc, Czechia. http://hdl.handle.net/20.500.12708/121096 ( reposiTUm)

Templ, M., Hron, K., Menafoglio, A., Hruskova, K., & Filzmoser, P. (2015). Simplicial principal component analysis for density functions. CoDaWork 2015, L’Escala, Spain. http://hdl.handle.net/20.500.12708/121112 ( reposiTUm)

Gardlo, A., Hron, K., Templ, M., & Filzmoser, P. (2015). Imputation of rounded zeros for data from metabolomics. CoDaWork 2015, L’Escala, Spain. http://hdl.handle.net/20.500.12708/121113 ( reposiTUm)

Szabo, B., Templ, M., Filzmoser, P., Lehoczky, A., & Pongrácz, R. (2015). Biogeographical regions and their flowering phenological patterns across Europe. Phenology 2015, Kusadasi, Turkey. http://hdl.handle.net/20.500.12708/121179 ( reposiTUm)

Filzmoser, P., Croux, C., Hoffmann, I., & Serneels, S. (2015). High-dimensional regression and classification with sparse partial robust M estimation (IPS067). Isi Wsc World Statistics Congress 2015, Rio de Janeiro, Brazil. http://hdl.handle.net/20.500.12708/120336 ( reposiTUm)

Filzmoser, P. (2015). Robust statistical methods for high-dimensional data. Isi Wsc World Statistics Congress 2015, Rio de Janeiro, Brazil. http://hdl.handle.net/20.500.12708/121127 ( reposiTUm)

Filzmoser, P. (2014). Identifikation multivariater Ausreißer bei Intervalldaten. Stochastik - Workshop Innsbruck (in Kooperation mit TU Dresden), Innsbruck, Austria. http://hdl.handle.net/20.500.12708/120832 ( reposiTUm)

Filzmoser, P., Reimann, C., & Birke, M. (2014). Statistical aspects when analyzing geochemical compositions. EGU European Geosciences Union General Assembly 2014, Vienna, Austria. http://hdl.handle.net/20.500.12708/120924 ( reposiTUm)

Filzmoser, P. (2014). PCA and beyond. Seminar for students of the Palacký University, Velké Losiny, EU. http://hdl.handle.net/20.500.12708/120923 ( reposiTUm)

Filzmoser, P. (2014). Statistical analysis of interval compositional data. Seminar at the Lisbon University of Technology, Lisbon, Portugal, EU. http://hdl.handle.net/20.500.12708/120925 ( reposiTUm)

Filzmoser, P. (2014). Robust statistics: theoretical and practical considerations. Seminar for students of the Palacký University, Dolni Morava, Cz, EU. http://hdl.handle.net/20.500.12708/120929 ( reposiTUm)

Templ, M., Hron, K., & Filzmoser, P. (2014). The Problem of Missing Values and Rounded Zeros in Compositional Data. Joint Statistical Meeting, Vancouver, Canada, Non-EU. http://hdl.handle.net/20.500.12708/120791 ( reposiTUm)

Filzmoser, P. (2014). Opportunities of compositional data analysis in chemometrics. CAC 2014 - 14th Conference on Chemometrics in Analytical Chemistry, Richmond, Virginia, EU. http://hdl.handle.net/20.500.12708/120919 ( reposiTUm)

Filzmoser, P. (2014). Identi cation of multivariate outliers in interval data. Seminar at the Palacky University, Olomouc / Czech Republic, Non-EU. http://hdl.handle.net/20.500.12708/120922 ( reposiTUm)

Filzmoser, P. (2014). Spline interpolation. Seminar at the Palacky University, Olomouc / Czech Republic, Non-EU. http://hdl.handle.net/20.500.12708/120920 ( reposiTUm)

Filzmoser, P. (2014). Generalized additive models. Seminar at the Palacky University, Olomouc / Czech Republic, Non-EU. http://hdl.handle.net/20.500.12708/120921 ( reposiTUm)

Templ, M., Alfons, A., & Filzmoser, P. (2014). Robust Estimation of Income and Social Indicators with Tail Modelling. Seminar in Applied Mathematics, Palacky University Olomouc, EU. http://hdl.handle.net/20.500.12708/120713 ( reposiTUm)

Tolosana-Delgado, R., van den Boogaart, K. G., Filzmoser, P., Hron, K., & Templ, M. (2014). Compositional regression: an overview. IAMG2014 - 16th Annual Conference of the International Association for Mathematical Geosciences, New Delhi, Non-EU. http://hdl.handle.net/20.500.12708/121012 ( reposiTUm)

Brito, P., Filzmoser, P., & Hron, K. (2014). Exploratory data analysis for interval compositional data. 4th Workshop in Symbolic Data Analysis (SDA 2014), Taipei, China, Non-EU. http://hdl.handle.net/20.500.12708/121007 ( reposiTUm)

Szabo, B., Lehoczky, A., Filzmoser, P., Templ, M., Szentkirályi, F., Pongrácz, R., Ortner, T., Mert, M. C., & Czúcz, B. (2014). From South to North: flowering phenological responses at different geographical latitudes in 12 European countries. European Geosciences Union General Assembly 2014, Wien, Austria. http://hdl.handle.net/20.500.12708/120757 ( reposiTUm)

Grad-Gyenge, L., Werthner, H., & Filzmoser, P. (2014). Spreading Activation for Rating Estimation in Recommender Systems. The 15th International Conference on Electronic Commerce and Web Technologies (EC-Web 2014), München, Germany. http://hdl.handle.net/20.500.12708/85935 ( reposiTUm)

Bögl, M., Aigner, W., Filzmoser, P., Lammarsch, T., Miksch, S., & Rind, A. (2013). Visual Analytics for Model Selection in Time Series Analysis. IEEE Conference on Visual Analytics Science and Technology (IEEE VAST), Atlanta, GA, USA, Non-EU. http://hdl.handle.net/20.500.12708/85611 ( reposiTUm)

Varmuza, K., & Filzmoser, P. (2013). Variable selection and its strict evaluation. CC 2013 Conferentia Chemometrica 2013, Sopron / Hungary, EU. http://hdl.handle.net/20.500.12708/120583 ( reposiTUm)

Mert, M. C., Filzmoser, P., & Hron, K. (2013). Principal balances with sparse PCA. ODAM 2013 - Olomoucian Days of Applied Mathematics, Olomouc, EU. http://hdl.handle.net/20.500.12708/120621 ( reposiTUm)

Hron, K., Filzmoser, P., & Fiserová, E. (2013). Correlation analysis for compositional data using classical and robust methods. ODAM 2013 - Olomoucian Days of Applied Mathematics, Olomouc, EU. http://hdl.handle.net/20.500.12708/120620 ( reposiTUm)

Filzmoser, P. (2013). Linear and non-linear methods for regression and classification. Workshop at the University of Debrecen, Hungary, EU. http://hdl.handle.net/20.500.12708/120619 ( reposiTUm)

Filzmoser, P. (2013). Stepwise variable selection in robust regression with compositional explanatory variables. ODAM 2013 - Olomoucian Days of Applied Mathematics, Olomouc, EU. http://hdl.handle.net/20.500.12708/120617 ( reposiTUm)

Filzmoser, P. (2013). Outlier detection in compositional data with structural zeros. ODAM 2013 - Olomoucian Days of Applied Mathematics, Olomouc, EU. http://hdl.handle.net/20.500.12708/120618 ( reposiTUm)

Kynčlová, P., Filzmoser, P., & Hron, K. (2013). Vector autoregression for compositiona; time series. ODAM 2013 - Olomoucian Days of Applied Mathematics, Olomouc, EU. http://hdl.handle.net/20.500.12708/120622 ( reposiTUm)

Kalidova, A., Hron, K., Templ, M., & Filzmoser, P. (2013). Replacement of Missing Values and Rounded Zeros in High-Dimensional Compositional Data with Application to Metabolomics. ODAM 2013 - Olomoucian Days of Applied Mathematics, Olomouc, EU. http://hdl.handle.net/20.500.12708/120467 ( reposiTUm)

Filzmoser, P. (2013). Sparse regression and classi cation methods for high-dimensional data. Seminar at the Vienna PhD-School of Informatics, Technical University Vienna, Austria. http://hdl.handle.net/20.500.12708/120581 ( reposiTUm)

Filzmoser, P. (2013). Sparse multivariate statistical methods for high-dimensional data. Seminar at the University of Bergen, Bergen, Norway, EU. http://hdl.handle.net/20.500.12708/120927 ( reposiTUm)

Filzmoser, P. (2013). Multivariate Regression und Klassi kation mit Anwendungen aus der Chemometrie. Herbstseminar der Wiener Biometrischen Sektion, Vienna, Austria. http://hdl.handle.net/20.500.12708/120928 ( reposiTUm)

Filzmoser, P. (2013). Concepts of compositional data analysis. Seminar at the Institute for Water Quality, Resource and Waste Management, Vienna, Austria. http://hdl.handle.net/20.500.12708/120926 ( reposiTUm)

Filzmoser, P. (2013). Support vector machine. Seminar at the Palacky University, Olomouc / Czech Republic, Non-EU. http://hdl.handle.net/20.500.12708/120930 ( reposiTUm)

Filzmoser, P. (2013). Robust dimension reduction for high-dimensional data. Universität Olomouc, Olomouc, EU. http://hdl.handle.net/20.500.12708/120597 ( reposiTUm)

Filzmoser, P. (2013). Advanced methods for regression and classi cation, and how to use them in R. ECO Winter School 2013, Kalmar, Sweden, EU. http://hdl.handle.net/20.500.12708/120611 ( reposiTUm)

Filzmoser, P., Croux, C., & Fritz, H. (2013). Sparse and robust principal component analysis. DAGStat 2013, Freiburg, Deutschland, EU. http://hdl.handle.net/20.500.12708/120612 ( reposiTUm)

Filzmoser, P. (2013). Robust variable selection in linear regression with compositional explanatory variables. Seminar at the University of Geneve, Genf, Schweiz, Non-EU. http://hdl.handle.net/20.500.12708/120614 ( reposiTUm)

Filzmoser, P. (2013). Variable selection in regression models. Universität Olomouc, Olomouc, EU. http://hdl.handle.net/20.500.12708/120613 ( reposiTUm)

Filzmoser, P. (2013). Computational statistics: The ATC/ICD project and applications. Workshop on Innovative Methods for Evidence Based Decision Making in Healthcare, Vienna, Austria. http://hdl.handle.net/20.500.12708/120615 ( reposiTUm)

Filzmoser, P. (2013). Robust Linear Regression for Compositional Data. ICORS 2013 International Conference on Robust Statistics, Saint Petersburg, Russia, Non-EU. http://hdl.handle.net/20.500.12708/120616 ( reposiTUm)

Todorov, V., & Filzmoser, P. (2013). Multivariate robust partial M-regression. ICORS 2013 International Conference on Robust Statistics, Saint Petersburg, Russia, Non-EU. http://hdl.handle.net/20.500.12708/120625 ( reposiTUm)

Hron, K., & Filzmoser, P. (2013). Exploring compositional data with the robust compositional biplot. SIS 2013 - Advances in Latent Variables - Methods, Models and Applications, Brescia, Italy, EU. http://hdl.handle.net/20.500.12708/120624 ( reposiTUm)

Kynčlová, P., Filzmoser, P., & Hron, K. (2013). How to Model Compositional Time Series from the Official Statistics Chair: Österreichische Statistiktage 2013, Statistik Austria, Wien, Austria. http://hdl.handle.net/20.500.12708/120626 ( reposiTUm)

Monti, G., Hron, K., Filzmoser, P., & Templ, M. (2013). Covariance-Based Outlier Detection for Compositional Data with Structural Zeros: Application to Italian Survey of Household Income and Wealth Data. SIS 2013 - Advances in Latent Variables - Methods, Models and Applications, Brescia, Italy, EU. http://hdl.handle.net/20.500.12708/120537 ( reposiTUm)

de la Rosa de Saa, S., Filzmoser, P., Gil, M. A., & Lubiano, M. A. (2013). Fuzzy rating or fuzzy linguistic? ODAM 2013 - Olomoucian Days of Applied Mathematics, Olomouc, EU. http://hdl.handle.net/20.500.12708/120623 ( reposiTUm)

Varmuza, K., Dehmer, M., & Filzmoser, P. (2013). Empirical modeling of mass spectral features by molecular descriptors. CC 2013 Conferentia Chemometrica 2013, Sopron / Hungary, EU. http://hdl.handle.net/20.500.12708/120582 ( reposiTUm)

Filzmoser, P., Gschwandtner, M., & Todorov, V. (2012). Review of sparse methods in regression and classification with application to chemometrics. Seminar at the National Insitute of Chemistry, Ljubljana / Slovenia, EU. http://hdl.handle.net/20.500.12708/120279 ( reposiTUm)

Filzmoser, P., Ruiz-Gazen, A., & Thomas-Agnan, C. (2012). Identification of local multivariate outliers. Workshop on Statistical Methods for Dependent Data, Witten / Germany, EU. http://hdl.handle.net/20.500.12708/120280 ( reposiTUm)

Filzmoser, P., Gschwandtner, M., & Todorov, V. (2012). Sparse statistical methods: theory, applications, software. ECO Summer School, Verona / Italy, EU. http://hdl.handle.net/20.500.12708/120283 ( reposiTUm)

Filzmoser, P., Croux, C., & Fritz, H. (2012). Robust Sparse Principal Component Analysis. Austrian Statistical Society, Vienna, Austria. http://hdl.handle.net/20.500.12708/120281 ( reposiTUm)

Filzmoser, P., & Hron, K. (2012). Compositional data analysis: challenges for environment sciences. University Venice, Venice / Italy, Austria. http://hdl.handle.net/20.500.12708/120282 ( reposiTUm)

Filzmoser, P., Neykov, N., & Neytchev, P. (2012). Robust ultrahigh-dimensional variable selection through trimming. International Conference on Robust Statistics, Parma, EU. http://hdl.handle.net/20.500.12708/120284 ( reposiTUm)

Neykov, N., Filzmoser, P., & Neytchev, P. (2012). Robust estimation in high demensional GLMs through trimming. Workshop on Statistical Methods for Dependent Data, Witten / Germany, EU. http://hdl.handle.net/20.500.12708/120287 ( reposiTUm)

Filzmoser, P. (2012). Compositional data analysis: Consequences for Chemometrics. Afrodatat 2012, Stellenbosch / South Africa, Non-EU. http://hdl.handle.net/20.500.12708/120285 ( reposiTUm)

Filzmoser, P. (2012). Introduction to concepts of robust statistics. Seminar at the Palacky University, Olomouc / Czech Republic, Non-EU. http://hdl.handle.net/20.500.12708/120286 ( reposiTUm)

Endel, G., Pfeffer, N., Wilbacher, I., Filzmoser, P., Endel, F., Eisl, A., Dorda, W., Duftschmid, G., Grossmann, W., Schober, E., & Waldhör, T. (2012). Burden of disease of Diabetes Mellitus - consequences for capacity planning. 9th HTAi Annual Meeting. HTA in Integrated Care for a Patient Centered System, Bilbao / Spain, EU. http://hdl.handle.net/20.500.12708/120288 ( reposiTUm)

Liebmann, B., Todeschini, R., Cansonni, V., Filzmoser, P., & Varmuza, K. (2012). Variable selection by the LASSO method. Conference on Chemometrics in Analytic Chemistry, Budapest / Hungary, EU. http://hdl.handle.net/20.500.12708/120289 ( reposiTUm)

Filzmoser, P. (2012). Regression and Classification Methods for High-dimensional Data. Seminar at the Palacky University, Olomouc / Czech Republic, Non-EU. http://hdl.handle.net/20.500.12708/120291 ( reposiTUm)

Alfons, A., Croux, C., & Filzmoser, P. (2012). Robust maximum correlation based on projection pursuit. International Conference on Robust Statistics, Parma, EU. http://hdl.handle.net/20.500.12708/120292 ( reposiTUm)

Todorov, V., & Filzmoser, P. (2012). rrcovHD: Moving rrcov to high dimensions. International Conference on Robust Statistics, Parma, EU. http://hdl.handle.net/20.500.12708/120293 ( reposiTUm)

Alfons, A., Croux, C., & Filzmoser, P. (2012). A projection-pursuit algorithm for robust maximum correlation estimators. Statistische Woche 2012, TU Wien, Austria. http://hdl.handle.net/20.500.12708/120297 ( reposiTUm)

Todorov, V., & Filzmoser, P. (2012). Sparse and robust partial least squares regression. Statistische Woche 2012, TU Wien, Austria. http://hdl.handle.net/20.500.12708/120296 ( reposiTUm)

Gschwandtner, M., & Filzmoser, P. (2012). Outlier detection by the use of the regularized MCD estimator. Statistische Woche 2012, TU Wien, Austria. http://hdl.handle.net/20.500.12708/120298 ( reposiTUm)

Hron, K., & Filzmoser, P. (2012). Classical and robust correlation analysis of compositional data. Statistische Woche 2012, TU Wien, Austria. http://hdl.handle.net/20.500.12708/120299 ( reposiTUm)

Hron, K., & Filzmoser, P. (2012). Robust diagnostics of fuzzy clustering results using the compositional approach. International Conference on Soft Methods in Probability and Statistics SMPS2012, Konstanz / Germany, Austria. http://hdl.handle.net/20.500.12708/120302 ( reposiTUm)

Todorov, V., & Filzmoser, P. (2012). Comparing classical and robust sparse PCA. International Conference on Soft Methods in Probability and Statistics SMPS2012, Konstanz / Germany, Austria. http://hdl.handle.net/20.500.12708/120303 ( reposiTUm)

Katschnig, H., Endel, F., Endel, G., Weibold, B., Scheffel, S., & Filzmoser, P. (2012). Dementia and pathways of health service utilisation in Austria: A record linkage study in a country with a fragmented provider payment system. 22nd Alzheimer Europe Conference, Vienna, Austria. http://hdl.handle.net/20.500.12708/120304 ( reposiTUm)

Hron, K., Filzmoser, P., Templ, M., van den Boogaart, G., & Tolosana-Delgado, R. (2012). A unified approach to classical and robust regression for compositional data. 5th International Conference of the ERCIM WG on COMPUTING & STATISTICS, Oviedo / Spain, EU. http://hdl.handle.net/20.500.12708/120306 ( reposiTUm)

Todorov, V., & Filzmoser, P. (2012). Sparse and robust partial least squares regression. 5th International Conference of the ERCIM WG on COMPUTING & STATISTICS, Oviedo / Spain, EU. http://hdl.handle.net/20.500.12708/120305 ( reposiTUm)

Schroeder, F., Braumann, A., & Filzmoser, P. (2012). Robust variable selection for linear regression models with compositional data. Statistische Woche 2012, TU Wien, Austria. http://hdl.handle.net/20.500.12708/120300 ( reposiTUm)

Boubela, R., Kalcher, K., Huf, W., Moser, E., Windischberger, C., & Filzmoser, P. (2012). A highly parallelized statistical analysis of fMRI data in R. Statistische Woche 2012, TU Wien, Austria. http://hdl.handle.net/20.500.12708/120295 ( reposiTUm)

Filzmoser, P., & Hron, K. (2011). Robuste multivariate Methoden für die Analyse von Kompositionsdaten. Workshop TU Dresden - TU Wien, Dresden, EU. http://hdl.handle.net/20.500.12708/119696 ( reposiTUm)

Hron, K., & Filzmoser, P. (2011). Mathematical elements in robust statistics for CoDa. Congress of the Spanish Royal Mathematical Society, Avila, EU. http://hdl.handle.net/20.500.12708/119694 ( reposiTUm)

Gschwandtner, M., Filzmoser, P., Croux, C., & Haesbroeck, G. (2011). A robust approach to regularized discriminant analysis. Statistische Tage 2011, Graz, Austria. http://hdl.handle.net/20.500.12708/119802 ( reposiTUm)

Alfons, A., & Filzmoser, P. (2011). Robust variable selection with application in the social sciences. Dutch/Flemish Classification Society Spring Meeting, Antwerpen, EU. http://hdl.handle.net/20.500.12708/119797 ( reposiTUm)

Neykov, N., Cizek, P., Filzmoser, P., & Neytchev, P. (2011). Robust quantile regression estimator through trimming. International Conference on Robust Statistics in Valladolid, Spanien, EU. http://hdl.handle.net/20.500.12708/119798 ( reposiTUm)

Filzmoser, P. (2011). Sparse regression and classification methods for high-dimensional data. University of Valladolid, Valladolid, EU. http://hdl.handle.net/20.500.12708/119796 ( reposiTUm)

Filzmoser, P. (2011). Finding relevant descriptors. Workshop “Molecular Descriptors,” Wien, Austria. http://hdl.handle.net/20.500.12708/119857 ( reposiTUm)

Katschnig, H., Endel, G., Endel, F., Filzmoser, P., & Weibold, B. (2011). Identifying psychiatric patients’ pathways of care: linking service use data for psychiatric and non-psychiatric services for the total population of a province of Austria. SHIP Biennial Conference, St Andrews, EU. http://hdl.handle.net/20.500.12708/119845 ( reposiTUm)

Todorov, V., & Filzmoser, P. (2011). Sparse and robust methods for discrimination in high dimensions. Statistische Tage 2011, Graz, Austria. http://hdl.handle.net/20.500.12708/119843 ( reposiTUm)

Filzmoser, P. (2011). Robust statistics: Concepts, methods, applications, and computation. SEAMS-GMU 2011 International Conference on Mathematics and Its Applications, Yogyakarta, Non-EU. http://hdl.handle.net/20.500.12708/119779 ( reposiTUm)

Filzmoser, P., Croux, C., & Fritz, H. (2011). Robust sparse principal component analysis based on projection-pursuit. International Conference on Robust Statistics in Valladolid, Spanien, EU. http://hdl.handle.net/20.500.12708/119778 ( reposiTUm)

Filzmoser, P. (2011). Sparse multivariate methods for high-dimensional data. 7th International Symposium on Computer Applications and Chemometrics in Analytical Chemistry, Sümeg, EU. http://hdl.handle.net/20.500.12708/119782 ( reposiTUm)

Filzmoser, P. (2011). Multivariate statistics using R. University of Salatiga, Salatiga, Non-EU. http://hdl.handle.net/20.500.12708/119780 ( reposiTUm)

Filzmoser, P. (2011). Robust sparse principal component analysis. Statistische Tage 2011, Graz, Austria. http://hdl.handle.net/20.500.12708/119781 ( reposiTUm)

Templ, M., Alfons, A., & Filzmoser, P. (2011). Robust semi-parametric estimation of economic and social indicators. 58th Session of the ISI Conference, Dublin, EU. http://hdl.handle.net/20.500.12708/119739 ( reposiTUm)

Filzmoser, P. (2011). Analyzing high-dimensional data, with application to chemometrics. University of Valladolid, Valladolid, EU. http://hdl.handle.net/20.500.12708/119786 ( reposiTUm)

Filzmoser, P. (2011). Regression and classification methods for high-dimensional data with application to chemometrics. Conferencias Statistical Robustness, Oviedo, EU. http://hdl.handle.net/20.500.12708/119783 ( reposiTUm)

Pascoal, C., Oliveira, M. R., Filzmoser, P., Pacheco, A., & Valadas, R. (2011). A new approach for variable selection in robust PCA, applied to anomaly detection in internet traffic flows. International Conference on Robust Statistics in Valladolid, Spanien, EU. http://hdl.handle.net/20.500.12708/119800 ( reposiTUm)

Todorov, V., Trendafilov, N., & Filzmoser, P. (2011). Sparse methods for robust discrimination in high dimensions. International Conference on Robust Statistics in Valladolid, Spanien, EU. http://hdl.handle.net/20.500.12708/119799 ( reposiTUm)

Hron, K., & Filzmoser, P. (2011). Linear regression with compositional explanatory variables using the logratio approach. International Conference of Probability and Statistics, Smolenice, EU. http://hdl.handle.net/20.500.12708/119801 ( reposiTUm)

Endel, F., Endel, G., Filzmoser, P., Weibold, B., & Katschnig, H. (2011). Health service record linkage in a situation of multiple social health insurance institutions: the case of Austria. SHIP Biennial Conference, St Andrews, EU. http://hdl.handle.net/20.500.12708/119844 ( reposiTUm)

Berger, W., Piringer, H., Filzmoser, P., & Gröller, E. (2011). Uncertainty-Aware Exploration of Continuous Parameter Spaces Using Multivariate Prediction. EuroVis 2011, Bergen, Norway, EU. http://hdl.handle.net/20.500.12708/85231 ( reposiTUm)

Templ, M., Kowarik, A., Filzmoser, P., & Alfons, A. (2011). A computational and methodological framework for visualisation and imputation of missing values: the R-package VIM. Statistische Tage 2011, Graz, Austria. http://hdl.handle.net/20.500.12708/119510 ( reposiTUm)

Alfons, A., Templ, M., & Filzmoser, P. (2010). Pareto tail modeling for social inclusion indicators. 6th AMELI Meeting, Trier, EU. http://hdl.handle.net/20.500.12708/119544 ( reposiTUm)

Katschnig, H., Endel, G., Endel, F., Filzmoser, P., & Weibold, B. (2010). Identifying psychiatric patients’ pathways through the health care system by record linkage after pseudonymisation. 26th PCSI annual conference, München, EU. http://hdl.handle.net/20.500.12708/119556 ( reposiTUm)

Filzmoser, P. (2010). Robust multivariate methods for compositional data. DAGStat 2010, Dortmund, EU. http://hdl.handle.net/20.500.12708/119490 ( reposiTUm)

Wieser, R., Filzmoser, H., Filzmoser, P., Alfons, A., Baaske, W. E., & Mader, W. (2010). How to improve the Quality of Life in Central European Municipalities. International Conference on Indicators and Survey Methodology, Wien, Austria. http://hdl.handle.net/20.500.12708/119489 ( reposiTUm)

Filzmoser, P. (2010). The “chemometrics” package in R - Application in multivariate calibration and classification. Chemometrics Workshop, Wien, Austria. http://hdl.handle.net/20.500.12708/119491 ( reposiTUm)

Hron, K., & Filzmoser, P. (2010). Robust regression for compositional data. International Conference on Robust Statistics, Parma, EU. http://hdl.handle.net/20.500.12708/119493 ( reposiTUm)

Neykov, N., Filzmoser, P., & Neytchev, P. (2010). Robust joint modeling of the mean and dispersion through trimming. International Conference on Robust Statistics, Parma, EU. http://hdl.handle.net/20.500.12708/119492 ( reposiTUm)

Alfons, A., Templ, M., & Filzmoser, P. (2010). Application of the R package simFrame for statistical simulation to EU-SILC. 4th AMELI Meeting, Wien, Austria. http://hdl.handle.net/20.500.12708/119546 ( reposiTUm)

Pfeffer, N., Eisl, A., Endel, F., Filzmoser, P., Scholler, C., & Weisser, A. (2010). Identification of diagnose-related procedure bundles in outpatient care using statistical methods. 26th PCSI annual conference, München, EU. http://hdl.handle.net/20.500.12708/119557 ( reposiTUm)

Weisser, A., Endel, F., Endel, G., & Filzmoser, P. (2010). Results of the project ATC-ICD. 26th PCSI annual conference, München, EU. http://hdl.handle.net/20.500.12708/119559 ( reposiTUm)

Filzmoser, P. (2010). Robust multivariate methods for high-dimensional data. Chemometrics in Analytical Chemistry (CAC 2010), Antwerpen, EU. http://hdl.handle.net/20.500.12708/119560 ( reposiTUm)

Alfons, A., Templ, M., & Filzmoser, P. (2010). Text Von Andi. AMELI Meeting in Wien, Wien, Austria. http://hdl.handle.net/20.500.12708/119357 ( reposiTUm)

Zechner, S., Filzmoser, P., Templ, M., & Alfons, A. (2010). Visualization of Indicators in R with application to EUSILC. 4th AMELI Meeting, Wien, Austria. http://hdl.handle.net/20.500.12708/119359 ( reposiTUm)

Meraner, A., Templ, M., & Filzmoser, P. (2010). Outlier Detection for Semi-continuous Variables. AMELI Meeting in Wien, Wien, Austria. http://hdl.handle.net/20.500.12708/119358 ( reposiTUm)

Templ, M., Kowarik, A., & Filzmoser, P. (2010). IRMI: An open-source solution for imputation of complex data using robust methods. European Conference on Quality in Official Statistics 2010, Helsinki, EU. http://hdl.handle.net/20.500.12708/119421 ( reposiTUm)

Todorov, V., & Filzmoser, P. (2010). Robust discrimination in high dimensions. International Conference on Robust Statistics, Parma, EU. http://hdl.handle.net/20.500.12708/119498 ( reposiTUm)

Templ, M., Holzer, J., Filzmoser, P., & Alfons, A. (2009). Robust methods for the estimation of selected Laeken indicators. 3rd AMELI meeting, Olten, Non-EU. http://hdl.handle.net/20.500.12708/118998 ( reposiTUm)

Alfons, A., Templ, M., & Filzmoser, P. (2009). State-of-the-Art and recent developments in Visualisation of Missing Values and Indicators. 3rd AMELI meeting, Olten, Non-EU. http://hdl.handle.net/20.500.12708/119002 ( reposiTUm)

Kraft, S., Filzmoser, P., & Templ, M. (2009). Simulation of a Population for the European Living and Income Conditions Survey. 3rd AMELI meeting, Olten, Non-EU. http://hdl.handle.net/20.500.12708/119000 ( reposiTUm)

Filzmoser, H., & Filzmoser, P. (2009). ErfolgsVision: Ergebnisdarstellung. Workshop: Erfolgsfaktoren für Gemeinden, Bad Zell, Austria. http://hdl.handle.net/20.500.12708/119205 ( reposiTUm)

Filzmoser, P. (2009). Exploratory Data Analysis und Visualization with R. Workshop “R” in Teaching and Empirical Research, Wien, Austria. http://hdl.handle.net/20.500.12708/119203 ( reposiTUm)

Filzmoser, P. (2009). ErfolgsVision: Statistische Methodik. Workshop: Erfolgsfaktoren für Gemeinden, Bad Zell, Austria. http://hdl.handle.net/20.500.12708/119204 ( reposiTUm)

Boubela, R., Filzmoser, P., & Piringer, H. (2009). Visplore and R: a symbiosis of powerfull visualization and statistical processing. Young Statisticians Meeting, Piran, Slowenien, EU. http://hdl.handle.net/20.500.12708/119215 ( reposiTUm)

Filzmoser, P. (2009). Robust Statistics: Concepts, Methods, Applications, and Software. COST Spring School, Mieres, EU. http://hdl.handle.net/20.500.12708/119207 ( reposiTUm)

Boubela, R., Filzmoser, P., & Piringer, H. (2009). Integrating R into the InfoVis System Visplore. useR! 2009, Rennes, EU. http://hdl.handle.net/20.500.12708/119208 ( reposiTUm)

Filzmoser, P. (2009). Robust Exploratory Factor Analysis as a Reliable Statistical Tool in IS Research. KIMEP International Research Conference, Almaty, Non-EU. http://hdl.handle.net/20.500.12708/119206 ( reposiTUm)

Filzmoser, P. (2009). Statistical Practices for Environmental Monitoring. 57th Session of the International Statistical Institute, Durban, Non-EU. http://hdl.handle.net/20.500.12708/119209 ( reposiTUm)

Filzmoser, P. (2009). Statistische Analyse von Kompositionsdaten. Institut für Statistik, Graz, Austria. http://hdl.handle.net/20.500.12708/119216 ( reposiTUm)

Filzmoser, P. (2009). Applied Environmental Statistics with Focus on Exploratory Data Analysis. IASC-ERS Summer School on Computational Aspects in Environmental Statistics, Pamporovo, EU. http://hdl.handle.net/20.500.12708/119212 ( reposiTUm)

Filzmoser, P. (2008). Outlier Identification and Robust PCA for Compositional Data. Seminar of the Statistics Department, Belarusian State University, Minsk, Non-EU. http://hdl.handle.net/20.500.12708/118622 ( reposiTUm)

Templ, M., Filzmoser, P., & Reimann, C. (2008). Cluster Analysis: Data preparation? Which algorithm? How many clusters? Lifestat 2008, München, EU. http://hdl.handle.net/20.500.12708/118614 ( reposiTUm)

Filzmoser, P., Ruiz-Gazen, A., Thomas-Agnan, C., & Reimann, C. (2008). Tools for local multivariate outlier detection. 1st Workshop of the ERCIM Working Group on Computing and Statistics, Neuchatel, Non-EU. http://hdl.handle.net/20.500.12708/118624 ( reposiTUm)

Reimann, C., & Filzmoser, P. (2008). Principal Component Analysis (PCA) and Factor Analysis (FA) with geochemical data: problems and possibilities. Seminar or the Norwegian Geological Survey, Trondheim, Non-EU. http://hdl.handle.net/20.500.12708/118623 ( reposiTUm)

Filzmoser, P. (2008). Defining background: Which statistical method ? Outliers versus extreme values. Eurosoil 2008, Wien, Austria. http://hdl.handle.net/20.500.12708/118625 ( reposiTUm)

Templ, M., & Filzmoser, P. (2008). Visualization and the use of R-Forge for collaborative research projects. AMELI kick-off meeting, Neuchatel, Non-EU. http://hdl.handle.net/20.500.12708/118612 ( reposiTUm)

Filzmoser, P. (2008). Robust statistics: a clever approach for using the useful data information. 4th International Symposium on Computer Applications and Chemometrics in Analytical Chemistry, Balatonalmadi, EU. http://hdl.handle.net/20.500.12708/118626 ( reposiTUm)

Baaske, W. E., Filzmoser, P., Mader, W., & Wieser, R. (2008). Landwirtschaft als Erfolgsfaktor für Kommunen? Ergebnisse aus Bevölkerungsbefragungen in Bürgerbeteiligungsprozessen. ÖGA-Jahrestagung 2008, Wien, EU. http://hdl.handle.net/20.500.12708/118627 ( reposiTUm)

Kraft, S., Templ, M., & Filzmoser, P. (2008). Simulation. AMELI Meeting, Wiesbaden, EU. http://hdl.handle.net/20.500.12708/118783 ( reposiTUm)

Templ, M., Alfons, A., & Filzmoser, P. (2008). Recent Developments in Visualization : Focus on Missing Values and Maps. AMELI Meeting, Wiesbaden, EU. http://hdl.handle.net/20.500.12708/118782 ( reposiTUm)

Filzmoser, P. (2008). Robust fitting of mixtures: The approach based on the Trimmed Likelihood Estimator. The 32nd Annual Conference of the German Classification Society, Hamburg, EU. http://hdl.handle.net/20.500.12708/118961 ( reposiTUm)

Alfons, A., Templ, M., & Filzmoser, P. (2008). Complex Survey Data Sets: Visualization of Missing Values in R. Young European Statisticians Workshop, Eindhoven, EU. http://hdl.handle.net/20.500.12708/118798 ( reposiTUm)

Alfons, A., Templ, M., & Filzmoser, P. (2008). Visualization Software for EU-SILC Data and Laeken Indicators. AMELI Meeting, Wiesbaden, EU. http://hdl.handle.net/20.500.12708/118797 ( reposiTUm)

Filzmoser, P. (2007). Robust PCA for Flat Data. International Workshop on Computational and Financial Econometrics, Genf, Non-EU. http://hdl.handle.net/20.500.12708/118254 ( reposiTUm)

Ruiz-Gazen, A., Thomas-Agnan, C., Filzmoser, P., & Reimann, R. (2007). Exploratory Tools for Spatial Multivariate Outlier Detection. 6th Spatial Econometrics and Statistics Workshop, Dijon, EU. http://hdl.handle.net/20.500.12708/118255 ( reposiTUm)

Filzmoser, P. (2007). Robuste Schätzung am Beispiel von Hauptkomponentenanalyse. Seminar of the Department of Mathematical Stochastics, Dresden, EU. http://hdl.handle.net/20.500.12708/118256 ( reposiTUm)

Filzmoser, P. (2007). Robust Principal Components. Jubilee International Conference 60 years Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, EU. http://hdl.handle.net/20.500.12708/118258 ( reposiTUm)

Filzmoser, P. (2007). Data Quality Issues for Statistical Methods. 11th Meeting of CEN/TG 230/WG 2, Wien, Austria. http://hdl.handle.net/20.500.12708/118257 ( reposiTUm)

Templ, M., & Filzmoser, P. (2007). Visualisation of Missing Values and Robust Imputation in Environmental Surveys. TIES 2007, 18th annual meeting of the International Environmetrics Society, Mikulov, EU. http://hdl.handle.net/20.500.12708/118260 ( reposiTUm)

Hron, K., & Filzmoser, P. (2007). Outlier Detection for Compositional Data and Applications to Environmetrics. TIES 2007, 18th annual meeting of the International Environmetrics Society, Mikulov, EU. http://hdl.handle.net/20.500.12708/118259 ( reposiTUm)

Filzmoser, P., Reimann, C., Ruiz-Gazen, A., & Thomas-Agnan, C. (2007). Exploratory Tools for Spatial Multivariate Outlier Detection. International Conference on Robust Statistics (ICORS 2007), Buenos Aires, Non-EU. http://hdl.handle.net/20.500.12708/118263 ( reposiTUm)

Templ, M., Filzmoser, P., & Reimann, C. (2007). Problems and Possibilities of Cluster Analysis: Application on Geochemical Data. ROeS Seminar 2007, Bern, Non-EU. http://hdl.handle.net/20.500.12708/118262 ( reposiTUm)

Todorov, V., & Filzmoser, P. (2007). Robust Statistic for the One-way MANOVA. TIES 2007, 18th annual meeting of the International Environmetrics Society, Mikulov, EU. http://hdl.handle.net/20.500.12708/118261 ( reposiTUm)

Filzmoser, P. (2007). Package mvoutlier for Multivariate Outlier Detection. International Workshop on Robust Statistics and R, Treviso, Italy, Austria. http://hdl.handle.net/20.500.12708/118264 ( reposiTUm)

Fritz, H., Filzmoser, P., & Templ, M. (2007). Robust Estimation of Missing Values. International Workshop on Robust Statistics and R, Treviso, Italy, Austria. http://hdl.handle.net/20.500.12708/118265 ( reposiTUm)

Filzmoser, P. (2007). Robustness for Compositional Data. Seminar für Statistik, Dortmund, EU. http://hdl.handle.net/20.500.12708/118289 ( reposiTUm)

Filzmoser, P. (2007). Robustness for Large Data Sets: New Challenges in High Dimension. Seminar of the Department of Statistics, Univ. Toulouse I, Toulouse, EU. http://hdl.handle.net/20.500.12708/118165 ( reposiTUm)

Filzmoser, P. (2006). Outlier Detection with Application to Geochemistry. The R User Conference 2006, Wien, EU. http://hdl.handle.net/20.500.12708/117930 ( reposiTUm)

Filzmoser, P. (2006). Outlier Identification in High Dimensions. International Conference on Robust Statistics (ICORS 2006), Lissabon, EU. http://hdl.handle.net/20.500.12708/117931 ( reposiTUm)

Filzmoser, P. (2006). Analysing Multivariate Data: Methods and their Piftalls. Arsenal Research, Wien, Austria. http://hdl.handle.net/20.500.12708/117939 ( reposiTUm)

Filzmoser, P. (2006). What can Robust Statistics Offer for Practice? Geological Survey of Norway, Trondheim, Non-EU. http://hdl.handle.net/20.500.12708/117940 ( reposiTUm)

Filzmoser, P. (2006). Projection Pursuit Algorithms for Robust Multivariate Methods. Robust Classification and Discrimination with High Dimensional Data, Florenz, EU. http://hdl.handle.net/20.500.12708/117915 ( reposiTUm)

Filzmoser, P. (2006). Outlier Detection in Very High Dimension. University of Liege, Belgien, EU. http://hdl.handle.net/20.500.12708/117968 ( reposiTUm)

Filzmoser, P. (2006). Extremwerte oder Ausreißer. Seminar of Department of Applied Statistics, Leuven, EU. http://hdl.handle.net/20.500.12708/117929 ( reposiTUm)

Filzmoser, P. (2006). Outliers or Extremes. Seminar of Department of Applied Statistics, Leuven, EU. http://hdl.handle.net/20.500.12708/117928 ( reposiTUm)

Templ, M., & Filzmoser, P. (2006). Stability of Cluster Analysis. The R User Conference 2006, Wien, EU. http://hdl.handle.net/20.500.12708/118078 ( reposiTUm)

Filzmoser, P. (2005). Multivariate Outlier Detection with Application to Geochemistry. Seminar of the Department of Statistics, Univ. Toulouse, Toulouse, Austria. http://hdl.handle.net/20.500.12708/117509 ( reposiTUm)

Filzmoser, P. (2005). The Package pcaPP: PCA by Projection Pursuit. International Workshop on Robust Statistics and R, Treviso, Italy, Austria. http://hdl.handle.net/20.500.12708/117508 ( reposiTUm)

Filzmoser, P. (2005). Multivariate und robuste Statistik in der Praxis. Forum Junge Statistik, Austrian Statistical Society, Vienna, Austria. http://hdl.handle.net/20.500.12708/117506 ( reposiTUm)

Filzmoser, P. (2005). Analyzing Data with Robust Statistical Methods. VRVis Center, Vienna, Austria. http://hdl.handle.net/20.500.12708/117507 ( reposiTUm)

Filzmoser, P. (2005). Robuste Statistik zur Erkennung multivariater Ausreißer. Wissenwertes aus der Mathematik, Wien, Austria. http://hdl.handle.net/20.500.12708/117510 ( reposiTUm)

Filzmoser, P. (2005). Statistical Analysis of Data from the MIDCC Project, Advanced Statistical Analysis. Workshop MIDCC, Mosonmagyarovar, Austria. http://hdl.handle.net/20.500.12708/117501 ( reposiTUm)

Filzmoser, P. (2005). The Projection Pursuit Approach for Robust Multivariate Analysis. ISDS Kolloquium, University of Vienna, Austria. http://hdl.handle.net/20.500.12708/117503 ( reposiTUm)

Filzmoser, P. (2005). Partial Robust Regression versus Robust Partial Least Squares. Perspectives in Modern Statistical Inference III, Mikulov, Czech Republic, Austria. http://hdl.handle.net/20.500.12708/117505 ( reposiTUm)

Filzmoser, P. (2005). Partial Robust M-Regression. International Conference on Robust Statistics, Parma, EU. http://hdl.handle.net/20.500.12708/117504 ( reposiTUm)

Neykov, N., Filzmoser, P., Dimova, R., & Neytchev, P. (2004). Mixture of GLMs and the trimmed likelihood methodology. Compstat 2004, Prag, Austria. http://hdl.handle.net/20.500.12708/116882 ( reposiTUm)

Filzmoser, P. (2004). An Adaptive Method for Multivariate Outlier Detection. International Conference on Robust Statistics, Parma, EU. http://hdl.handle.net/20.500.12708/116857 ( reposiTUm)

Filzmoser, P. (2004). A Multivariate Outlier Detection Method. Konferenz CDAM (Computer Data Analysis and Modeling), Minsk, Austria. http://hdl.handle.net/20.500.12708/116858 ( reposiTUm)

Filzmoser, P. (2004). Statistical Analysis of Data from the MIDCC Project. Workshop MIDCC, Mosonmagyarovar, Austria. http://hdl.handle.net/20.500.12708/116853 ( reposiTUm)

Filzmoser, P. (2004). Statistical Methods in Developing Quality Assessment Criteria. Workshop "What’s fishy about the Water Framework Directive?, Stockholm, Austria. http://hdl.handle.net/20.500.12708/116854 ( reposiTUm)

Filzmoser, P. (2004). Konzepte der robusten Statistik mit Anwendungen. Seminarreihe von VRVis Center, Wien, Austria. http://hdl.handle.net/20.500.12708/116855 ( reposiTUm)

Filzmoser, P. (2004). Partial Least Squares Regression. Workshop “Robust Analysis of Large Data Sets,” Banff, Kanada, Austria. http://hdl.handle.net/20.500.12708/116856 ( reposiTUm)

Filzmoser, P. (2003). A statistical method for finding indicators of water quality. Symposium on "How to assess and monitor ecological quality in freshwaters, Helsinki, Austria. http://hdl.handle.net/20.500.12708/116335 ( reposiTUm)

Filzmoser, P. (2003). Multivariate Outlier Detection and Visualization. StatGIS 2003: Interfacing Geostatistics, GIS and Spatial Databases, Pörtschach, Kärnten, Austria. http://hdl.handle.net/20.500.12708/116334 ( reposiTUm)

Filzmoser, P. (2003). Measures of Association based on Projection Pursuit. International Conference on Robust Statistics (ICORS 2003), Antwerpen, Austria. http://hdl.handle.net/20.500.12708/116331 ( reposiTUm)

Filzmoser, P. (2003). Univariate and Multivariate Outlier Detection with Application to Geochemical Data. RMED’03: Workshop on Robust Modeling of Environmental Data, Vorau, Austria. http://hdl.handle.net/20.500.12708/116330 ( reposiTUm)

Filzmoser, P. (2003). Robuste Faktorenanalyse und erweiterte Modelle. Institut f. Ökonometrie, Operations Research und Systemtheorie, TU wien, Austria. http://hdl.handle.net/20.500.12708/116329 ( reposiTUm)

Filzmoser, P. (2003). Robust factor analysis and extended models. Institut für Angewandte Mathematik, Masaryk Universität, Brno, Brno, Austria. http://hdl.handle.net/20.500.12708/116328 ( reposiTUm)

Filzmoser, P. (2003). Maße für Assoziation basierend auf Projection Pursuit. Österreichische Statistiktage 2003, Wien, Austria. http://hdl.handle.net/20.500.12708/116386 ( reposiTUm)

Filzmoser, P. (2002). Robust factor analysis. International Conference on Robust Statistics, Parma, EU. http://hdl.handle.net/20.500.12708/116192 ( reposiTUm)

Kavsek, B., & Filzmoser, P. (2002). PLS - Regression und ihre Anwendungen. Österreichische Statistiktage 2002, Wien, Austria. http://hdl.handle.net/20.500.12708/115919 ( reposiTUm)

Filzmoser, P. (2002). Robust fitting of additive and multiplicative models. Universite Libre de Bruxelles, Bruxelles, Austria. http://hdl.handle.net/20.500.12708/115906 ( reposiTUm)

Filzmoser, P. (2002). Robust estimation of the parameters in the FANOVA model. Instituto Superior Tecnico Lisbon, Lissabon, Austria. http://hdl.handle.net/20.500.12708/115907 ( reposiTUm)

Filzmoser, P. (2002). Dimension reduction of the explanatory variables in (robust) multiple linear regression. XXII International Seminar on Stability Problems for Stochastic Models and Seminar on Statistical Data Analysis (SDA 2002), Varna, Austria. http://hdl.handle.net/20.500.12708/115905 ( reposiTUm)

Christodoulides, P., & Filzmoser, P. (2002). Hauptebenenanalyse - eine Erweiterung der Hauptkomponentenanalyse. Österreichische Statistiktage 2002, Wien, Austria. http://hdl.handle.net/20.500.12708/115917 ( reposiTUm)

Berichte

Brodinova, S., Ortner, T., Filzmoser, P., Zaharieva, M., & Breiteneder, C. (2015). Evaluation of Robust PCA for Supervised Audio Outlier Detection (CS-2015-2). http://hdl.handle.net/20.500.12708/38539 ( reposiTUm)

Hoffmann, I., Serneels, S., Filzmoser, P., & Croux, C. (2015). Sparse partial robust M-regression (CS-2015-1). http://hdl.handle.net/20.500.12708/38588 ( reposiTUm)

Filzmoser, P., Gussenbauer, J., & Templ, M. (2015). Detecting outliers in household consumption survey data (Deliverable 4. Contract with world bank (1157976)). http://hdl.handle.net/20.500.12708/38563 ( reposiTUm)

Filzmoser, P., Templ, M., & Gussenbauer, J. (2015). Short Overview on Outlier Detection Methods (Deliverable 1 Contract with world bank (1157976)). http://hdl.handle.net/20.500.12708/38564 ( reposiTUm)

Hron, K., Menafoglio, A., Templ, M., Hruzova, K., & Filzmoser, P. (2014). Simplicial principal component analysis for density functions in Bayes spaces (MOX-report 25/2014). http://hdl.handle.net/20.500.12708/38070 ( reposiTUm)

Munnich, R., Zins, S., Alfons, A., Bruch, C., Filzmoser, P., Graf, M., Hulliger, B., Kolb, J.-P., Lehtonen, R., Lussmann, D., Meraner, A., Myrskylä, M., Nedyalkova, D., Schoch, T., Templ, M., Valaste, M., & Veijanen, A. (2011). Policy Recommendations and Methodological Report (Europ. Commission,FP7-SSH-2007-217322,WP6-D10-1). http://hdl.handle.net/20.500.12708/37378 ( reposiTUm)

Templ, M., Kowarik, A., & Filzmoser, P. (2011). EM-based regression imputation using robust methods (Europ. Commission,FP7-SSH-2007-21732,WP4-D4.2-8). http://hdl.handle.net/20.500.12708/36967 ( reposiTUm)

Alfons, A., Templ, M., Filzmoser, P., & Holzer, J. (2011). Semi-parametric robust estimation (Europ. Commission,FP7-SSH-2007-21732,WP4-D4.2-3). http://hdl.handle.net/20.500.12708/36966 ( reposiTUm)

Alfons, A., Filzmoser, P., Meraner, A., & Templ, M. (2011). Robust distribution fitting (European Commission,FP7-SSH-2007-217322,WP4-D4.1-3). http://hdl.handle.net/20.500.12708/36965 ( reposiTUm)

Templ, M., Hron, K., & Filzmoser, P. (2011). Robust imputation for compositional data (Europ. Commission,FP7-SSH-2007-21732,WP4-D4.2-11). http://hdl.handle.net/20.500.12708/36968 ( reposiTUm)

Meraner, A., Filzmoser, P., & Templ, M. (2011). Robust methods for semi-continuous data (Europ. Commission,FP7-SSH-2007-21732,WP4-D4.2-12). http://hdl.handle.net/20.500.12708/36969 ( reposiTUm)

Hulliger, B., Alfons, A., Filzmoser, P., Meraner, A., Schoch, T., & Templ, M. (2011). R programmes for robust procedures including manual (Europ. Commission,FP7-SSH-2007-21732,WP4-D4.1). http://hdl.handle.net/20.500.12708/36963 ( reposiTUm)

Hulliger, B., Alfons, A., Filzmoser, P., Meraner, A., Schoch, T., & Templ, M. (2011). Robust methodology for Laeken indicators (Europ. Commission,FP7-SSH-2007-21732,WP4-D4.2). http://hdl.handle.net/20.500.12708/36964 ( reposiTUm)

Alfons, A., Filzmoser, P., Kraft, S., Templ, M., Kolb, J.-P., & Münnich, R. (2011). Design-based simulations (European Commission,FP7-SSH-2007-217322,WP6-D6.1-2). http://hdl.handle.net/20.500.12708/36971 ( reposiTUm)

Graf, M., Alfons, A., Bruch, C., Filzmoser, P., Hulliger, B., Lehtonen, R., Meindl, B., Münnich, R., Schoch, T., Templ, M., Valaste, M., Wenger, A., & Zins, S. (2011). State-of-the-art of indicators on poverty and social exclusion the Laeken indicators (Europ. Commission,FP7-SSH-2007-21732,WP1-D1.1). http://hdl.handle.net/20.500.12708/36961 ( reposiTUm)

Alfons, A., Filzmoser, P., & Templ, M. (2011). Summary of the State-of-the-art in visualisation of the European Laeken Indicators (European Commission,FP7-SSH-2007-217322,WP1-D1.1-6). http://hdl.handle.net/20.500.12708/36962 ( reposiTUm)

Alfons, A., Burgard, J. P., Filzmoser, P., Hulliger, B., Kolb, J.-P., Kraft, S., Münnich, R., Schoch, T., & Templ, M. (2011). The AMELI simulation study (European Commission,FP7-SSH-2007-217322,WP6-D6.1). http://hdl.handle.net/20.500.12708/36970 ( reposiTUm)

Alfons, A., Templ, M., Filzmoser, P., & Holzer, J. (2011). Robust Pareto tail modeling for the estimation of indicators on social exclusion using the R package laeken (CS-2011-2). http://hdl.handle.net/20.500.12708/36831 ( reposiTUm)

Hulliger, B., Schoch, T., Alfons, A., Holzer, J., Filzmoser, P., Meraner, A., & Templ, M. (2011). WP4: Robustness (Europ. Commission,FP7-SSH-2007-21732,WP7-D7.1-10). http://hdl.handle.net/20.500.12708/36988 ( reposiTUm)

Alfons, A., Templ, M., & Filzmoser, P. (2011). Simulation of EU-SILC population data using simPopulation (Europ. Commission,FP7-SSH-2007-21732,WP10-D10.3-3). http://hdl.handle.net/20.500.12708/37009 ( reposiTUm)

Alfons, A., Templ, M., & Filzmoser, P. (2011). Applications of statistical simulation (Europ. Commission,FP7-SSH-2007-21732,WP10-D10.3-2). http://hdl.handle.net/20.500.12708/37008 ( reposiTUm)

Hulliger, B., Alfons, A., Bruch, C., Filzmoser, P., Graf, M., Kolb, J.-P., Lehtonen, R., Lussmann, D., Meraner, A., Münnich, R., Myrskylä, M., Nedyalkova, D., Schoch, T., Templ, M., Valaste, M., Veijanen, A., & Zins, S. (2011). Report on the simulation results (Europ. Commission,FP7-SSH-2007-21732,WP7-D7.1). http://hdl.handle.net/20.500.12708/36976 ( reposiTUm)

Alfons, A., Filzmoser, P., Hulliger, B., Kolb, J.-P., Kraft, S., Münnich, R., & Templ, M. (2011). Synthetic data generation of SILC (European Commission,FP7-SSH-2007-217322,WP6-D6.2-3). http://hdl.handle.net/20.500.12708/36973 ( reposiTUm)

Alfons, A., Filzmoser, P., Kraft, S., & Templ, M. (2011). The R package simFrame (European Commission,FP7-SSH-2007-217322,WP6-D6.1-3). http://hdl.handle.net/20.500.12708/36972 ( reposiTUm)

Alfons, A., Templ, M., Filzmoser, P., & Holzer, J. (2011). Robust Pareto tail modeling with package laeken (Europ. Commission,FP7-SSH-2007-21732,WP10-D10.3-6). http://hdl.handle.net/20.500.12708/37017 ( reposiTUm)

Alfons, A., Templ, M., & Filzmoser, P. (2011). Robust semiparametric estimation of economic indicators from survey samples (CS-2011-5). http://hdl.handle.net/20.500.12708/37020 ( reposiTUm)

Hulliger, B., Alfons, A., Bruch, C., Filzmoser, P., Graf, M., Kolb, J.-P., Lehtonen, R., Lussmann, D., Meraner, A., Münnich, R., Myrskylä, M., Nedyalkova, D., Schoch, T., Templ, M., Valaste, M., Veijanen, A., & Zins, S. (2011). Report on the simulation results: Appendix (Europ. Commission,FP7-SSH-2007-21732,WP7-D7.1-App). http://hdl.handle.net/20.500.12708/36989 ( reposiTUm)

Templ, M., Hulliger, B., Alfons, A., Lussmann, D., & Filzmoser, P. (2011). Visualisation tools (Europ. Commission,FP7-SSH-2007-21732,WP8-D8.2). http://hdl.handle.net/20.500.12708/36990 ( reposiTUm)

Templ, M., Alfons, A., Filzmoser, P., Graf, M., Hulliger, B., Kolb, J.-P., Lehtonen, R., Münnich, R., Nedyalkova, D., Schoch, T., Veijanen, A., & Zins, S. (2011). R packages plus manual (Europ. Commission,FP7-SSH-2007-21732,WP10-D10.3). http://hdl.handle.net/20.500.12708/37004 ( reposiTUm)

Hulliger, B., Zechner, S., Templ, M., & Filzmoser, P. (2011). Evaluation Plots: Revision and extensions (Europ. Commission,FP7-SSH-2007-21732,WP8-D8.2-3). http://hdl.handle.net/20.500.12708/36999 ( reposiTUm)

Templ, M., Alfons, A., & Filzmoser, P. (2011). Diagnostic tools for missing values (Europ. Commission,FP7-SSH-2007-21732,WP8-D8.2-8). http://hdl.handle.net/20.500.12708/37003 ( reposiTUm)

Croux, C., Filzmoser, P., & Fritz, H. (2011). Robust sparse principal component analysis (SM-2011-2). http://hdl.handle.net/20.500.12708/37005 ( reposiTUm)

Filzmoser, P., & Todorov, V. (2011). Review of robust multivariate statistical methods in high dimension (SM-2011-1). http://hdl.handle.net/20.500.12708/37006 ( reposiTUm)

Alfons, A., Kraft, S., Templ, M., & Filzmoser, P. (2010). Simulation of synthetic population data for household surveys with application to EU-SILC (CS-2010-1). http://hdl.handle.net/20.500.12708/36649 ( reposiTUm)

Templ, M., Kowarik, A., & Filzmoser, P. (2010). EM-based stepwise regression imputation using standard and robust methods (CS-2010-3). http://hdl.handle.net/20.500.12708/36658 ( reposiTUm)

Alfons, A., Templ, M., & Filzmoser, P. (2010). Simulation of EU-SILC population data: using the R package simPopulation (CS-2010-5). http://hdl.handle.net/20.500.12708/36775 ( reposiTUm)

Fritz, H., Filzmoser, P., & Croux, C. (2010). A comparison of algorithms for the multivariate L1-median (CS-2010-4). http://hdl.handle.net/20.500.12708/36776 ( reposiTUm)

Liebmann, B., Filzmoser, P., & Varmuza, K. (2009). Robust and classical PLS regression compared (CS-2009-8). http://hdl.handle.net/20.500.12708/35728 ( reposiTUm)

Filzmoser, P., Hron, K., & Templ, M. (2009). Discriminant analysis for compositional data and robust parameter estimation (SM-2009-3). http://hdl.handle.net/20.500.12708/35731 ( reposiTUm)

Filzmoser, P., Hron, K., & Reimann, C. (2009). Univariate statistical analysis of environmental data: Problems and possibilities (SM-2009-2). http://hdl.handle.net/20.500.12708/35730 ( reposiTUm)

Treiblmaier, H., & Filzmoser, P. (2009). Benefits from using continuous rating scales in online survey research (SM-2009-4). http://hdl.handle.net/20.500.12708/35732 ( reposiTUm)

Todorov, V., & Filzmoser, P. (2009). An object-oriented framework for robust multivariate analysis (CS-2009-7). http://hdl.handle.net/20.500.12708/35727 ( reposiTUm)

Treiblmaier, H., & Filzmoser, P. (2009). Exploratory factor analysis revisited: How robust methods support the detection of hidden multivariate data structures in IS research (SM-2009-5). http://hdl.handle.net/20.500.12708/35733 ( reposiTUm)

Alfons, A., Filzmoser, P., Kraft, S., & Templ, M. (2009). Intermediate report on the data generation mechanism and of the design of the simulation study (AMELI Project Deliverable 6.1). http://hdl.handle.net/20.500.12708/35663 ( reposiTUm)

Alfons, A., Filzmoser, P., Kraft, S., & Templ, M. (2009). Summary of the state-of-the-art in simulation in survey statistics (AMELI Project Deliverable 6.1, Part1). http://hdl.handle.net/20.500.12708/35672 ( reposiTUm)

Alfons, A., Filzmoser, P., Kraft, S., & Templ, M. (2009). Progress Report WP6-Simulation (AMELI Project Deliverable 6.1, Part 2). http://hdl.handle.net/20.500.12708/35673 ( reposiTUm)

Alfons, A., Templ, M., & Filzmoser, P. (2009). simFrame: An object-oriented framework for statistical simulation (CS-2009-1). http://hdl.handle.net/20.500.12708/35712 ( reposiTUm)

Alfons, A., Filzmoser, P., Kraft, S., & Templ, M. (2009). Generation of synthetic EU-SILC data and simulation (CS-2009-5). http://hdl.handle.net/20.500.12708/35708 ( reposiTUm)

Alfons, A., Filzmoser, P., Hulliger, B., Meindl, B., Schoch, T., & Templ, M. (2009). State-of-the-art in visualization of indicators in survey statistics (CS-2009-4). http://hdl.handle.net/20.500.12708/35709 ( reposiTUm)

Templ, M., Alfons, A., & Filzmoser, P. (2009). Tools for visualising data and aggregated information (CS-2009-3). http://hdl.handle.net/20.500.12708/35710 ( reposiTUm)

Alfons, A., Kraft, S., Filzmoser, P., & Templ, M. (2009). Progress report on simulation and synthetic data generation for EU-SILC data (CS-2009-6). http://hdl.handle.net/20.500.12708/35707 ( reposiTUm)

Alfons, A., Filzmoser, P., & Templ, M. (2009). Summary of the State-of-the-Art in Visualization of the European Laeken Indicators (AMELI Project Deliverable 8.1, Part 3). http://hdl.handle.net/20.500.12708/35612 ( reposiTUm)

Templ, M., & Filzmoser, P. (2008). Visualization of missing values using the R-package VIM (CS-2008-1). http://hdl.handle.net/20.500.12708/34992 ( reposiTUm)

Filzmoser, P., Liebmann, B., & Varmuza, K. (2008). Repeated double cross validation (CS-2008-4). http://hdl.handle.net/20.500.12708/35074 ( reposiTUm)

Filzmoser, P. (2008). Linear and nonlinear methods for regression and classification and applications in R (CS-2008-3). http://hdl.handle.net/20.500.12708/35073 ( reposiTUm)

Karacsony, Z., & Filzmoser, P. (2008). Asymptotic normality of kernel type regression estimators for random fields (MS-2008-1). http://hdl.handle.net/20.500.12708/35075 ( reposiTUm)

Filzmoser, P., & Hron, K. (2008). Correlation analysis for compositional data (SM-2008-2). http://hdl.handle.net/20.500.12708/35077 ( reposiTUm)

Filzmoser, P., Hron, K., Reimann, C., & Garrett, R. G. (2008). Robust factor analysis for compositional data (SM-2008-3). http://hdl.handle.net/20.500.12708/35078 ( reposiTUm)

Alfons, A., Baaske, W. E., Filzmoser, P., Mader, W., & Wieser, R. (2008). A context-sensitive method for robust model selection with application to analysing success factors of communities (CS-2008-6). http://hdl.handle.net/20.500.12708/35125 ( reposiTUm)

Wieser, R., & Filzmoser, P. (2008). Anwendung statistischer Verfahren auf einen kommunalen Datensatz (CS-2008-7). http://hdl.handle.net/20.500.12708/35264 ( reposiTUm)

Hron, K., Templ, M., & Filzmoser, P. (2008). Imputation of missing values for compositional data using classical and robust methods (SM-2008-4). http://hdl.handle.net/20.500.12708/35229 ( reposiTUm)

Filzmoser, P., Serneels, S., Maronna, R., & Van Espen, P. J. (2007). Robust multivariate methods in chemometrics (CS-2007-3). http://hdl.handle.net/20.500.12708/31775 ( reposiTUm)

Todorov, V., & Filzmoser, P. (2007). Robust statistic for the one-way MANOVA (CS-2007-7). http://hdl.handle.net/20.500.12708/31792 ( reposiTUm)

Filzmoser, P., Hron, K., & Reimann, C. (2007). Principal component analysis for compositional data with outliers (SM-2007-3). http://hdl.handle.net/20.500.12708/31779 ( reposiTUm)

Filzmoser, P., & Fritz, H. (2007). Exploring high-dimensional data with robust principal components (CS-2007-2). http://hdl.handle.net/20.500.12708/31776 ( reposiTUm)

Filzmoser, P., & Hron, K. (2007). Outlier detection for compositional data using robust methods (CS-2007-1). http://hdl.handle.net/20.500.12708/31777 ( reposiTUm)

Templ, M., Filzmoser, P., & Reimann, R. (2006). Cluster Analysis applied to regional geochemical data: Problems and possibilities, CS-2006-5. http://hdl.handle.net/20.500.12708/31765 ( reposiTUm)

Filzmoser, P., & Filzmoser, H. (2006). Ermittlung der Wahrscheinlichkeit einer Nichteinhaltung des Sicherheitsabstandes zwischen Ankerbohrungen, C-2006-4. http://hdl.handle.net/20.500.12708/31764 ( reposiTUm)

Filzmoser, P., Joossens, K., & Croux, C. (2006). Multiple group discriminant analysis: Robustness and error rate, CS-2006-1. http://hdl.handle.net/20.500.12708/31737 ( reposiTUm)

Filzmoser, P., Maronna, R., Dimova, R., & Werner, M. (2006). Outlier identification in high dimensions, CS-2006-3. http://hdl.handle.net/20.500.12708/31763 ( reposiTUm)

Neykov, N., Filzmoser, P., Dimova, R., & Neytchev, P. (2006). Robust fitting of mixtures using the Trimmed Likelihood Estimator, CS-2006-2. http://hdl.handle.net/20.500.12708/31734 ( reposiTUm)

Croux, C., Filzmoser, P., & Joossens, K. (2005). Robust linear discriminant analysis for multiple groups: influence and classification efficiencies, CS-2005-1. http://hdl.handle.net/20.500.12708/31736 ( reposiTUm)

van Helvoort, P. J., Filzmoser, P., & van Gaans, P. F. M. (2004). Sequential factor analysis as a new approach to multivariate analysis of heterogeneous geochemical datasets: an application to a bulk chemical characterization of fluvial deposits (Rhine-Meuse delta, the Netherlands), TS-04-1. http://hdl.handle.net/20.500.12708/31650 ( reposiTUm)

Croux, C., Filzmoser, P., & Oliveira, M. R. (2004). Projection-pursuit Estimators for Robust Principal Component Analysis, TS-04-4. http://hdl.handle.net/20.500.12708/31653 ( reposiTUm)

Serneels, S., Croux, C., Filzmoser, P., & Van Espen, P. J. (2004). Robust Partial M-Regression, TS-04-2. http://hdl.handle.net/20.500.12708/31651 ( reposiTUm)

Filzmoser, P. (2004). Identification of Multivariate Outliers: A Performance Study, TS-04-3. http://hdl.handle.net/20.500.12708/31652 ( reposiTUm)

Oliveira, M. R., Branco, J., Croux, C., & Filzmoser, P. (2003). Robust Redundancy analysis by alternating regression, TS-03-2. http://hdl.handle.net/20.500.12708/31597 ( reposiTUm)

Reimann, C., Filzmoser, P., & Garrett, R. G. (2003). Background and threshold - the need for visualisation, TS-03-1. http://hdl.handle.net/20.500.12708/31596 ( reposiTUm)

Croux, C., & Filzmoser, P. (2003). Projection pursuit based mesasures of association, TS-03-3. http://hdl.handle.net/20.500.12708/31600 ( reposiTUm)

Branco, J., Croux, C., Filzmoser, P., & Oliveira, M. R. (2003). Robust canonical correlations: a comparative study, TS-03-4. http://hdl.handle.net/20.500.12708/31598 ( reposiTUm)

Janauer, G., & Filzmoser, P. (2003). Statistical consideration for monitoring macrophytes in lakes according to the water framework directive: Towards minimising the survey effort, TS-03-6. http://hdl.handle.net/20.500.12708/31605 ( reposiTUm)

Filzmoser, P., Garrett, R. G., & Reimann, C. (2003). Multivariate outlier detection in exploration geochemistry, TS-03-5. http://hdl.handle.net/20.500.12708/31604 ( reposiTUm)

Filzmoser, P., & Viertl, R. (2002). Testing hypotheses with fuzzy data: The fuzzy p-value, RIS-2002-3. http://hdl.handle.net/20.500.12708/31550 ( reposiTUm)

Preprints

Neubauer, L., & Filzmoser, P. (2024). Rediscovering Bottom-Up: Effective Forecasting in Temporal Hierarchies. arXiv. https://doi.org/10.48550/arXiv.2407.02367 ( reposiTUm)

Parzer, R., Vana Gür, L., & Filzmoser, P. (2024). spar: Sparse Projected Averaged Regression in R. arXiv. https://doi.org/10.34726/8080 ( reposiTUm)

Parzer, R., Filzmoser, P., & Vana Gür, L. (2024). Data-Driven Random Projection and Screening for High-Dimensional Generalized Linear Models. arXiv. https://doi.org/10.34726/8079 ( reposiTUm)

Neubauer, L., & Filzmoser, P. (2024). Enhancing Forecasts Using Real-Time Data Flow and Hierarchical Forecast Reconciliation, with Applications to the Energy Sector. arXiv. https://doi.org/10.48550/arXiv.2411.01528 ( reposiTUm)

Puchhammer, P., Wilms, I., & Filzmoser, P. (2024). Sparse outlier-robust PCA for multi-source data. arXiv. http://hdl.handle.net/20.500.12708/210155 ( reposiTUm)

Oguamalam, J., Filzmoser, P., Hron, K., Menafoglio, A., & Radojicic, U. (2024). Robust functional PCA for density data. arXiv. https://doi.org/10.34726/8739 ( reposiTUm)

Neubauer, L., & Filzmoser, P. (2023). Improving Forecasts for Heterogeneous Time Series by “Averaging”, with Application to Food Demand Forecast. arXiv. https://doi.org/10.48550/arXiv.2306.07119 ( reposiTUm)

Parzer, R., Vana Gür, L., & Filzmoser, P. (2023). Sparse Projected Averaged Regression for High-Dimensional Data. arXiv. https://doi.org/10.34726/5489 ( reposiTUm)

Mayrhofer, M., & Filzmoser, P. (2022). Multivariate outlier explanations using Shapley values and Mahalanobis distances. arXiv. https://doi.org/10.34726/3163 ( reposiTUm)

Heiler, G., Hanbury, A., & Filzmoser, P. (2020). The impact of COVID-19 on relative changes in aggregated mobility using mobile-phone data (p. 14). arXiv. https://doi.org/10.48550/arXiv.2009.03798 ( reposiTUm)

Hochschulschriften

Filzmoser, P. (2001). Faktorenanalyse und ihre Robustifizierung [Professorial Dissertation, Technische Universität Wien]. reposiTUm. http://hdl.handle.net/20.500.12708/179031 ( reposiTUm)