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dc.contributor.authorTempl, Matthias-
dc.contributor.authorHron, Karel-
dc.contributor.authorFilzmoser, Peter-
dc.contributor.authorGardlo, Alžbӗta-
dc.date.accessioned2018-05-02T08:48:57Z-
dc.date.available2018-05-02T08:48:57Z-
dc.date.issued2016-
dc.identifier.issn0169-7439de_CH
dc.identifier.issn1873-3239de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/5691-
dc.description.abstractHigh-dimensional compositional data, multivariate observations carrying relative information, frequently contain values below a detection limit (rounded zeros). We introduce new model-based procedures for replacing these values with reasonable numbers, so that the completed data set is ready for use with statistical analysis methods that rely on complete data, such as regression or classification with high-dimensional explanatory variables. The procedures respect the geometry of compositional data and can be considered as alternatives to existing methods. Simulations show that especially in high-dimensions, the proposed methods outperform existing methods. Moreover, even for a large number of rounded zeros, the new methods lead to an improved quality of the data, which is important for further analyses. The usefulness of the procedure is demonstrated using a data example from metabolomics.de_CH
dc.language.isoende_CH
dc.publisherElsevierde_CH
dc.relation.ispartofChemometrics and Intelligent Laboratory Systemsde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subject.ddc510: Mathematikde_CH
dc.titleImputation of rounded zeros for high-dimensional compositional datade_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.2016.04.011de_CH
zhaw.funding.euNode_CH
zhaw.issue155de_CH
zhaw.originated.zhawNode_CH
zhaw.pages.end190de_CH
zhaw.pages.start183de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume2016de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
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Templ, M., Hron, K., Filzmoser, P., & Gardlo, A. (2016). Imputation of rounded zeros for high-dimensional compositional data. Chemometrics and Intelligent Laboratory Systems, 2016(155), 183–190. https://doi.org/10.1016/j.chemolab.2016.04.011
Templ, M. et al. (2016) ‘Imputation of rounded zeros for high-dimensional compositional data’, Chemometrics and Intelligent Laboratory Systems, 2016(155), pp. 183–190. Available at: https://doi.org/10.1016/j.chemolab.2016.04.011.
M. Templ, K. Hron, P. Filzmoser, and A. Gardlo, “Imputation of rounded zeros for high-dimensional compositional data,” Chemometrics and Intelligent Laboratory Systems, vol. 2016, no. 155, pp. 183–190, 2016, doi: 10.1016/j.chemolab.2016.04.011.
TEMPL, Matthias, Karel HRON, Peter FILZMOSER und Alžbӗta GARDLO, 2016. Imputation of rounded zeros for high-dimensional compositional data. Chemometrics and Intelligent Laboratory Systems. 2016. Bd. 2016, Nr. 155, S. 183–190. DOI 10.1016/j.chemolab.2016.04.011
Templ, Matthias, Karel Hron, Peter Filzmoser, and Alžbӗta Gardlo. 2016. “Imputation of Rounded Zeros for High-Dimensional Compositional Data.” Chemometrics and Intelligent Laboratory Systems 2016 (155): 183–90. https://doi.org/10.1016/j.chemolab.2016.04.011.
Templ, Matthias, et al. “Imputation of Rounded Zeros for High-Dimensional Compositional Data.” Chemometrics and Intelligent Laboratory Systems, vol. 2016, no. 155, 2016, pp. 183–90, https://doi.org/10.1016/j.chemolab.2016.04.011.


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