<div class="csl-bib-body">
<div class="csl-entry">Beisteiner, L. (2016). <i>Exploratory tools for cellwise outlier detection in compositional data with structural zeros</i> [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2016.37291</div>
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dc.identifier.uri
https://doi.org/10.34726/hss.2016.37291
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/6648
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dc.description
Abweichender Titel nach Übersetzung der Verfasserin/des Verfassers
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dc.description.abstract
The analysis of compositional data using the log-ratio approach is based on ratios between the compositional parts. Zeros in the parts thus cause severe difficulties for the analysis. Log-ratio transformations represent the compositional information into new coordinates. Outliers within these coordinates may be detected, however it remains unclear which particular parts of the composition led to the deviating ratios in question. To address this issue, the thesis presents four exploratory tools for identifying cellwise outliers in compositional data sets with structural zeros. In order to deal with structural zeros the proposed methods use robust imputation methods or split the data into subcompositions determined by their zero patterns. Ratios between parts are analyzed using an isometric log-ratio transformation or by observing pairwise log-ratios. Combining the results from robust regression analysis and robust distance calculations the approaches deduce row- and cellwise outliers within the original sample space. All four methods are applied on the household expenditure data from Albania and then compared. A close-to-reality simulation study is conducted to assess the performance of the different outlier detection algorithms.
en
dc.language
English
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dc.language.iso
en
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
compositional data analysis
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dc.subject
robust statistics
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dc.subject
outlier detection
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dc.title
Exploratory tools for cellwise outlier detection in compositional data with structural zeros
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dc.title.alternative
Explorative Methoden für die Erkennung von zellweisen Ausreißern in Kompositionsdaten mit strukturellen Nullen
de
dc.type
Thesis
en
dc.type
Hochschulschrift
de
dc.rights.license
In Copyright
en
dc.rights.license
Urheberrechtsschutz
de
dc.identifier.doi
10.34726/hss.2016.37291
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dc.contributor.affiliation
TU Wien, Österreich
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dc.rights.holder
Lukas Beisteiner
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dc.publisher.place
Wien
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tuw.version
vor
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tuw.thesisinformation
Technische Universität Wien
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tuw.publication.orgunit
E105 - Institut für Stochastik und Wirtschaftsmathematik
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dc.type.qualificationlevel
Diploma
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dc.identifier.libraryid
AC13253715
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dc.description.numberOfPages
86
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dc.identifier.urn
urn:nbn:at:at-ubtuw:1-4879
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dc.thesistype
Diplomarbeit
de
dc.thesistype
Diploma Thesis
en
dc.rights.identifier
In Copyright
en
dc.rights.identifier
Urheberrechtsschutz
de
tuw.advisor.staffStatus
staff
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item.fulltext
with Fulltext
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item.grantfulltext
open
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item.cerifentitytype
Publications
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item.cerifentitytype
Publications
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item.languageiso639-1
en
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item.openairecristype
http://purl.org/coar/resource_type/c_18cf
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item.openairecristype
http://purl.org/coar/resource_type/c_18cf
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item.openairetype
Thesis
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item.openairetype
Hochschulschrift
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item.openaccessfulltext
Open Access
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crisitem.author.dept
E105 - Institut für Stochastik und Wirtschaftsmathematik