Independent Component Analysis for Compositional Data

Christoph Muehlmann, Kamila Fačevicová, Alžběta Gardlo, Hana Janečková, Klaus Nordhausen

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

Abstract

Compositional data represent a specific family of multivariate data, where the information of interest is contained in the ratios between parts rather than in absolute values of single parts. The analysis of such specific data is challenging as the application of standard multivariate analysis tools on the raw observations can lead to spurious results. Hence, it is appropriate to apply certain transformations prior to further analysis. One popular multivariate data analysis tool is independent component analysis. Independent component analysis aims to find statistically independent components in the data and as such might be seen as an extension to principal component analysis. In this paper, we examine an approach of how to apply independent component analysis on compositional data by respecting the nature of the latter and demonstrate the usefulness of this procedure on a metabolomics dataset.

Original languageEnglish
Title of host publicationAdvances in Contemporary Statistics and Econometrics : Festschrift in Honor of Christine Thomas-Agnan
EditorsAbdelaati Daouia, Anne Ruiz-Gazen
Number of pages21
Place of PublicationCham
PublisherSpringer
Publication date2021
Pages525-545
ISBN (Print)978-3-030-73248-6, 978-3-030-73251-6
ISBN (Electronic)978-3-030-73249-3
DOIs
Publication statusPublished - 2021
Externally publishedYes
MoE publication typeA3 Book chapter

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2021.

Fields of Science

  • 112 Statistics and probability
  • 113 Computer and information sciences

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