Publication type: Conference other
Type of review: Peer review (abstract)
Title: Strategies to replace high proportions of zeros in compositional data
Authors: Filzmoser, Peter
Lubbe, Sugnet
Templ, Matthias
et. al: No
Conference details: 1st Conference on Information Technology and Data Science, CITDS, Debrecen, Hungary / Online, 6-8 November 2020
Issue Date: 7-Nov-2020
Language: English
Subjects: Compositional data; Biomic data; High-dimensional data; Rounded zeros; Imputation; Replacement
Subject (DDC): 005: Computer programming, programs and data
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 [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): Licence according to publishing contract
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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