Robust Sliced Inverse Regression Procedures

dc.contributor.author	Becker, Claudia	de
dc.contributor.author	Gather, Ursula	de
dc.contributor.author	Hilker, Torsten	de
dc.date.accessioned	2004-12-06T18:38:21Z
dc.date.available	2004-12-06T18:38:21Z
dc.date.issued	1998	de
dc.description.abstract	Sliced 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.extent	1372325 bytes
dc.format.extent	303122 bytes
dc.format.mimetype	application/pdf
dc.format.mimetype	application/postscript
dc.identifier.uri	http://hdl.handle.net/2003/4840
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-5417
dc.language.iso	en	de
dc.publisher	Universitätsbibliothek Dortmund	de
dc.subject	dimension reduction	en
dc.subject	high breakdown procedures	en
dc.subject	outliers	en
dc.subject.ddc	310	de
dc.title	Robust Sliced Inverse Regression Procedures	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access
eldorado.dnb.deposit	true

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