Record link: 
http://hdl.handle.net/20.500.12708/30121
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Title: 
Compositional Data Analysis in Chemometrics
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Citation: 
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
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Publisher DOI: 
10.1016/B978-0-12-409547-2.14591-3
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Publication Type: 
Book Contribution - Edited Volume Contribution
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Authors: 
Filzmoser, Peter 
Hron, Karel 
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Organisational Unit: 
E105-06 - Forschungsbereich Computational Statistics 
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Published in: 
Comprehensive Chemometrics. Chemical and Biochemical Data Analysis 
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ISBN: 
978-0-444-64166-3
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Date (published): 
2020
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Number of Pages: 
22
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Publisher: 
Elsevier
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Peer reviewed: 
Yes
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Keywords: 
Aitchison geometryBootstrapClassificationCompositional dataLinear discriminant analysisPartial least squaresPLS-DAPrincipal component analysisRegressionRelative informationValidation
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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.
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Additional information: 
Public link: https://www.elsevier.com/books/comprehensive-chemometrics/brown/978-0-444-64165-6
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Research Areas: 
Computational Materials Science: 100%
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Science Branch: 
Mathematik
Informatik
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Appears in Collections:
Book Contribution

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