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 |