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dc.contributor.authorBernholt, Thorsten-
dc.date.accessioned2006-01-25T12:51:23Z-
dc.date.available2006-01-25T12:51:23Z-
dc.date.issued2006-01-25T12:51:23Z-
dc.identifier.urihttp://hdl.handle.net/2003/22138-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-14253-
dc.description.abstractIn 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.extent308185 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen-
dc.relation.ispartofseriesSonderforschungsbereich 475;52/05-
dc.subjectalgorithmsen
dc.subjectcomplexity theoryen
dc.subjectcomputational statisticsen
dc.subjectrobust statisticsen
dc.subjectsearch heuristicsen
dc.subject.ddc004-
dc.titleRobust Estimators are Hard to Computeen
dc.typeTextde
dc.type.publicationtypereporten
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 475

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