Robust Estimators are Hard to Compute
| dc.contributor.author | Bernholt, Thorsten | |
| dc.date.accessioned | 2006-01-25T12:51:23Z | |
| dc.date.available | 2006-01-25T12:51:23Z | |
| dc.date.issued | 2006-01-25T12:51:23Z | |
| dc.description.abstract | In modern statistics, the robust estimation of parameters of a re- gression hyperplane is a central problem. Robustness means that the estimation is not or only slightly a®ected by outliers in the data. In this paper, it is shown that the following robust estimators are hard to compute: LMS, LQS, LTS, LTA, MCD, MVE, Constrained M es- timator, Projection Depth (PD) and Stahel-Donoho. In addition, a data set is presented such that the ltsReg-procedure of R has proba- bility less than 0.0001 of ¯nding a correct answer. Furthermore, it is described, how to design new robust estimators. | en |
| dc.format.extent | 308185 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/2003/22138 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-14253 | |
| dc.language.iso | en | |
| dc.relation.ispartofseries | Sonderforschungsbereich 475;52/05 | |
| dc.subject | algorithms | en |
| dc.subject | complexity theory | en |
| dc.subject | computational statistics | en |
| dc.subject | robust statistics | en |
| dc.subject | search heuristics | en |
| dc.subject.ddc | 004 | |
| dc.title | Robust Estimators are Hard to Compute | en |
| dc.type | Text | de |
| dc.type.publicationtype | report | en |
| dcterms.accessRights | open access |