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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-21934
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dc.contributor.authorLubbe, Sugnet-
dc.contributor.authorFilzmoser, Peter-
dc.contributor.authorTempl, Matthias-
dc.date.accessioned2021-03-04T15:07:02Z-
dc.date.available2021-03-04T15:07:02Z-
dc.date.issued2021-
dc.identifier.issn0169-7439de_CH
dc.identifier.issn1873-3239de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/21934-
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.de_CH
dc.language.isoende_CH
dc.publisherElsevierde_CH
dc.relation.ispartofChemometrics and Intelligent Laboratory Systemsde_CH
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/de_CH
dc.subjectImputationde_CH
dc.subjectCompositional data analysisde_CH
dc.subjectZero sum regressionde_CH
dc.subjectMicrobiome datade_CH
dc.subject.ddc510: Mathematikde_CH
dc.titleComparison of zero replacement strategies for compositional data with large numbers of zerosde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.1016/j.chemolab.2021.104248de_CH
dc.identifier.doi10.21256/zhaw-21934-
zhaw.funding.euNode_CH
zhaw.issue104248de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume210de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedStatistik und Quantitative Financede_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Lubbe, S., Filzmoser, P., & Templ, M. (2021). Comparison of zero replacement strategies for compositional data with large numbers of zeros. Chemometrics and Intelligent Laboratory Systems, 210(104248). https://doi.org/10.1016/j.chemolab.2021.104248
Lubbe, S., Filzmoser, P. and Templ, M. (2021) ‘Comparison of zero replacement strategies for compositional data with large numbers of zeros’, Chemometrics and Intelligent Laboratory Systems, 210(104248). Available at: https://doi.org/10.1016/j.chemolab.2021.104248.
S. Lubbe, P. Filzmoser, and M. Templ, “Comparison of zero replacement strategies for compositional data with large numbers of zeros,” Chemometrics and Intelligent Laboratory Systems, vol. 210, no. 104248, 2021, doi: 10.1016/j.chemolab.2021.104248.
LUBBE, Sugnet, Peter FILZMOSER und Matthias TEMPL, 2021. Comparison of zero replacement strategies for compositional data with large numbers of zeros. Chemometrics and Intelligent Laboratory Systems. 2021. Bd. 210, Nr. 104248. DOI 10.1016/j.chemolab.2021.104248
Lubbe, Sugnet, Peter Filzmoser, and Matthias Templ. 2021. “Comparison of Zero Replacement Strategies for Compositional Data with Large Numbers of Zeros.” Chemometrics and Intelligent Laboratory Systems 210 (104248). https://doi.org/10.1016/j.chemolab.2021.104248.
Lubbe, Sugnet, et al. “Comparison of Zero Replacement Strategies for Compositional Data with Large Numbers of Zeros.” Chemometrics and Intelligent Laboratory Systems, vol. 210, no. 104248, 2021, https://doi.org/10.1016/j.chemolab.2021.104248.


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