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dc.contributor.authorČίžek, Pavelen
dc.contributor.authorHärdle, Wolfgang Karlen
dc.date.accessioned2005-08-15-
dc.date.accessioned2009-07-23T14:42:49Z-
dc.date.available2009-07-23T14:42:49Z-
dc.date.issued2005-
dc.identifier.urihttp://hdl.handle.net/10419/25034-
dc.description.abstractMost dimension reduction methods based on nonparametric smoothing are highly sensitive to outliers and to data coming from heavy-tailed distributions. We show that the recently proposed methods by Xia et al. (2002) can be made robust in such a way that preserves all advantages of the original approach. Their extension based on the local one-step M-estimators is sufficiently robust to outliers and data from heavy tailed distributions, it is relatively easy to implement, and surprisingly, it performs as well as the original methods when applied to normally distributed data.en
dc.language.isoengen
dc.publisher|aHumboldt University of Berlin, Collaborative Research Center 649 - Economic Risk |cBerlinen
dc.relation.ispartofseries|aSFB 649 Discussion Paper |x2005,015en
dc.subject.ddc330en
dc.subject.keywordDimension reductionen
dc.subject.keywordNonparametric regressionen
dc.subject.keywordM-estimationen
dc.subject.stwNichtparametrisches Verfahrenen
dc.subject.stwRobustes Verfahrenen
dc.subject.stwSchätztheorieen
dc.subject.stwTheorieen
dc.titleRobust estimation of dimension reduction space-
dc.type|aWorking Paperen
dc.identifier.ppn496003585en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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