<div class="csl-bib-body">
<div class="csl-entry">Filzmoser, P., & Hron, K. (2020). Compositional Data Analysis in Chemometrics. In R. Tauler, B. Walczak, & S. Brown (Eds.), <i>Comprehensive Chemometrics. Chemical and Biochemical Data Analysis</i> (pp. 641–662). Elsevier. https://doi.org/10.1016/B978-0-12-409547-2.14591-3</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/30121
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dc.description
Public link: https://www.elsevier.com/books/comprehensive-chemometrics/brown/978-0-444-64165-6
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dc.description.abstract
This article presents an introduction to the field and methods of compositional data analysis. The basic concepts and ideas are explained, and the approach is motivated from a geometrical perspective. Multivariate methods that are relevant in chemometrics are considered from the point of view of compositional data analysis, such as principal component analysis, multivariate outlier detection, as well as regression and classification, also for the high-dimensional case. The article includes several examples which illustrate the use of this methodology for practical data analysis.
en
dc.publisher
Elsevier
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dc.subject
Aitchison geometryBootstrapClassificationCompositional dataLinear discriminant analysisPartial least squaresPLS-DAPrincipal component analysisRegressionRelative informationValidation
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dc.title
Compositional Data Analysis in Chemometrics
en
dc.type
Buchbeitrag
de
dc.type
Book Contribution
en
dc.relation.publication
Comprehensive Chemometrics. Chemical and Biochemical Data Analysis
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dc.relation.isbn
978-0-444-64166-3
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dc.description.startpage
641
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dc.description.endpage
662
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dc.type.category
Edited Volume Contribution
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tuw.booktitle
Comprehensive Chemometrics. Chemical and Biochemical Data Analysis
