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
eldorado.dnb.deposit	true

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