Comparison of zero replacement strategies for compositional data with large numbers of zeros

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dc.contributor.authorLubbe, Sugneten_ZA
dc.contributor.authorFilzmoser, Peteren_ZA
dc.contributor.authorTempl, Matthiasen_ZA
dc.date.accessioned2023-05-10T13:51:33Z
dc.date.available2023-05-10T13:51:33Z
dc.date.issued2021-03
dc.descriptionCITATION: Lubbe, S., Filzmoser, P. & Templ, M. 2021. Comparison of zero replacement strategies for compositional data with large numbers of zeros. Elsevier Chemometrics and Intelligent Laboratory Systems 210(2021):11 pages. doi.10.1016/j.chemolab.2021.104248en_ZA
dc.descriptionThe original publication is available at: sciencedirect.comen_ZA
dc.description.abstractModern 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.en_ZA
dc.description.versionPublisher’s versionen_ZA
dc.format.extent11 pages : illustrationsen_ZA
dc.identifier.citationLubbe, S., Filzmoser, P. & Templ, M. 2021. Comparison of zero replacement strategies for compositional data with large numbers of zeros. Elsevier Chemometrics and Intelligent Laboratory Systems 210(2021):11 pages.en_ZA
dc.identifier.issn0169-7439 (online)en_ZA
dc.identifier.otherdoi.10.1016/j.chemolab.2021.104248en_ZA
dc.identifier.urihttp://hdl.handle.net/10019.1/126893
dc.language.isoen_ZAen_ZA
dc.publisherElsevier B.V.en_ZA
dc.rights.holderAuthors retain copyrighten_ZA
dc.subjectStatistical modelsen_ZA
dc.subjectRegression analysisen_ZA
dc.subjectBioinformaticsen_ZA
dc.titleComparison of zero replacement strategies for compositional data with large numbers of zerosen_ZA
dc.typeArticleen_ZA
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