The Largest Nonidentifiable Outlier

dc.contributor.authorBecker, Claudiade
dc.contributor.authorGather, Ursulade
dc.date.accessioned2004-12-06T18:42:14Z
dc.date.available2004-12-06T18:42:14Z
dc.date.issued2000de
dc.description.abstractThe aim of detecting outliers in a multivariate sample can be pursued in different ways. We investigate here the performance of several simultaneous multivariate outlier identification rules based on robust estimators of location and scale. It has been shown that the use of estimators with high finite-sample breakdown point in such procedures yields a good behaviour with respect to the prevention of breakdown by the masking effect (Becker, Gather, 1999, J. Amer. Statist. Assoc. 94, 947-955). In this article, we investigate by simulation, at which distance from the center of an underlying model distribution outliers can be placed until certain simultaneous identifocation rules will detect them as outliers. We consider identification procedures based on the minimum volume ellipsoid, the minimum covariance determinant, and S-estimators.en
dc.format.extent185777 bytes
dc.format.extent415113 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.identifier.urihttp://hdl.handle.net/2003/5023
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-15047
dc.language.isoende
dc.publisherUniversitätsbibliothek Dortmundde
dc.subjecthigh breakdown point proceduresen
dc.subjectMCDen
dc.subjectMVEen
dc.subjectoutliersen
dc.subjectrobustnessen
dc.subjectS-estimatorsen
dc.subject.ddc310de
dc.titleThe Largest Nonidentifiable Outlieren
dc.title.alternativeA Comparison of Multivariate Simultaneous Outlier Identification Rulesen
dc.typeTextde
dc.type.publicationtypereporten
dcterms.accessRightsopen access

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