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dc.contributor.authorBecker, Claudiade
dc.contributor.authorGather, Ursulade
dc.contributor.authorHilker, Torstende
dc.date.accessioned2004-12-06T18:38:21Z-
dc.date.available2004-12-06T18:38:21Z-
dc.date.issued1998de
dc.identifier.urihttp://hdl.handle.net/2003/4840-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-5417-
dc.description.abstractSliced Inverse Regression (SIR) is a promising technique for the purpose of dimension reduction. Several properties of this relatively new method have been examined already, but little attention has been paid to robustness aspects. We show that SIR is very sensitive towards outliers in the data. Therefore a generalized estimation procedure which allows for robustness properties, especially for a high breakdown point, is proposed.en
dc.format.extent1372325 bytes-
dc.format.extent303122 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/postscript-
dc.language.isoende
dc.publisherUniversitätsbibliothek Dortmundde
dc.subjectdimension reductionen
dc.subjecthigh breakdown proceduresen
dc.subjectoutliersen
dc.subject.ddc310de
dc.titleRobust Sliced Inverse Regression Proceduresen
dc.typeTextde
dc.type.publicationtypereporten
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 475

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