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Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Comparison of zero replacement strategies for compositional data with large numbers of zeros
Authors: Lubbe, Sugnet
Filzmoser, Peter
Templ, Matthias
et. al: No
DOI: 10.1016/j.chemolab.2021.104248
Published in: Chemometrics and Intelligent Laboratory Systems
Volume(Issue): 210
Issue: 104248
Issue Date: 2021
Publisher / Ed. Institution: Elsevier
ISSN: 0169-7439
Language: English
Subjects: Imputation; Compositional data analysis; Zero sum regression; Microbiome data
Subject (DDC): 510: Mathematics
Abstract: Modern applications in chemometrics and bioinformatics result in compositional data sets with a high proportion of zeros. An example are microbiome data, where zeros refer to measurements below the detection limit of one count. When building statistical models, it is important that zeros are replaced by sensible values. Different replacement techniques from compositional data analysis are considered and compared by a simulation study and examples. The comparison also includes a recently proposed method (Templ, 2020) [1] based on deep learning. Detailed insights into the appropriateness of the methods for a problem at hand are provided, and differences in the outcomes of statistical results are discussed.
Fulltext version: Published version
License (according to publishing contract): CC BY-NC-ND 4.0: Attribution - Non commercial - No derivatives 4.0 International
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Appears in collections:Publikationen School of Engineering

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