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2005-10-12Buch DOI: 10.18452/3554
Robust adaptive estimation of dimension reduction space
dc.contributor.authorČížek, Pavel
dc.contributor.authorHärdle, Wolfgang Karl
dc.date.accessioned2017-06-15T21:22:14Z
dc.date.available2017-06-15T21:22:14Z
dc.date.created2005-10-12
dc.date.issued2005-10-12
dc.identifier.issn1436-1086
dc.identifier.urihttp://edoc.hu-berlin.de/18452/4206
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 MAVE and OPG methods by Xia et al. (2002) allow us to make them robust in a relatively straightforward way that preserves all advantages of the original approach. The best of the proposed robust modifications, which we refer to as MAVE-WMAD-R, is sufficiently robust to outliers and data from heavy tailed distributions, it is easy to implement, and surprisingly, it also outperforms the original method in small sample behaviour even when applied to normally distributed data.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.subjectnonparametric regressioneng
dc.subjectdimension reductioneng
dc.subjectminimum average variance estimatoreng
dc.subjectrobust estimationeng
dc.subjectmedian absolute deviationeng
dc.subjectL1 regressioneng
dc.subject.ddc330 Wirtschaft
dc.titleRobust adaptive estimation of dimension reduction space
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-10049716
dc.identifier.doihttp://dx.doi.org/10.18452/3554
local.edoc.container-titleSonderforschungsbereich 373: Quantification and Simulation of Economic Processes
local.edoc.pages20
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2003
local.edoc.container-issue1
local.edoc.container-year2003
local.edoc.container-erstkatid2135319-0

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