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dc.contributor.authorTempl, Matthias-
dc.contributor.authorHron, K.-
dc.contributor.authorFilzmoser, P.-
dc.date.accessioned2018-05-02T08:50:26Z-
dc.date.available2018-05-02T08:50:26Z-
dc.date.issued2016-
dc.identifier.issn0266-4763de_CH
dc.identifier.issn1360-0532de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/5693-
dc.description.abstractThe analysis of compositional data using the log-ratio approach is based on ratios between the compositional parts. Zeros in the parts thus cause serious difficulties for the analysis. This is a particular problem in case of structural zeros, which cannot be simply replaced by a non-zero value as it is done, e.g. for values below detection limit or missing values. Instead, zeros to be incorporated into further statistical processing. The focus is on exploratory tools for identifying outliers in compositional data sets with structural zeros. For this purpose, Mahalanobis distances are estimated, computed either directly for subcompositions determined by their zero patterns, or by using imputation to improve the efficiency of the estimates, and then proceed to the subcompositional and subgroup level. For this approach, new theory is formulated that allows to estimate covariances for imputed compositional data and to apply estimations on subgroups using parts of this covariance matrix. Moreover, the zero pattern structure is analyzed using principal component analysis for binary data to achieve a comprehensive view of the overall multivariate data structure. The proposed tools are applied to larger compositional data sets from official statistics, where the need for an appropriate treatment of zeros is obvious.de_CH
dc.language.isoende_CH
dc.publisherTaylor & Francisde_CH
dc.relation.ispartofJournal of Applied Statisticsde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subject.ddc510: Mathematikde_CH
dc.titleExploratory tools for outlier detection in compositional data with structural 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.doi10.1080/02664763.2016.1182135de_CH
zhaw.funding.euNode_CH
zhaw.issue4de_CH
zhaw.originated.zhawNode_CH
zhaw.pages.end752de_CH
zhaw.pages.start734de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume44de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
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Templ, M., Hron, K., & Filzmoser, P. (2016). Exploratory tools for outlier detection in compositional data with structural zeros. Journal of Applied Statistics, 44(4), 734–752. https://doi.org/10.1080/02664763.2016.1182135
Templ, M., Hron, K. and Filzmoser, P. (2016) ‘Exploratory tools for outlier detection in compositional data with structural zeros’, Journal of Applied Statistics, 44(4), pp. 734–752. Available at: https://doi.org/10.1080/02664763.2016.1182135.
M. Templ, K. Hron, and P. Filzmoser, “Exploratory tools for outlier detection in compositional data with structural zeros,” Journal of Applied Statistics, vol. 44, no. 4, pp. 734–752, 2016, doi: 10.1080/02664763.2016.1182135.
TEMPL, Matthias, K. HRON und P. FILZMOSER, 2016. Exploratory tools for outlier detection in compositional data with structural zeros. Journal of Applied Statistics. 2016. Bd. 44, Nr. 4, S. 734–752. DOI 10.1080/02664763.2016.1182135
Templ, Matthias, K. Hron, and P. Filzmoser. 2016. “Exploratory Tools for Outlier Detection in Compositional Data with Structural Zeros.” Journal of Applied Statistics 44 (4): 734–52. https://doi.org/10.1080/02664763.2016.1182135.
Templ, Matthias, et al. “Exploratory Tools for Outlier Detection in Compositional Data with Structural Zeros.” Journal of Applied Statistics, vol. 44, no. 4, 2016, pp. 734–52, https://doi.org/10.1080/02664763.2016.1182135.


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