Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/207
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dc.contributor.authorAntoch, J.-
dc.contributor.authorJANSSEN, Paul-
dc.date.accessioned2004-08-30T07:50:50Z-
dc.date.available2004-08-30T07:50:50Z-
dc.date.issued1989-
dc.identifier.citationStatist. Prob. Letters, 8(4), p. 355-362-
dc.identifier.urihttp://hdl.handle.net/1942/207-
dc.description.abstractFor the regression model Yi = m(xi)+var epsiloni, i = 1,…,n, robust nonparametric estimators are introduced and studied in Härdle and Gasser (1984). We show that these estimators can be viewed as regression M-quantiles. We then establish a probability inequality and a Bahadur representation for such quantiles and discuss some applications.-
dc.language.isoen-
dc.subjectMathematical Statistics-
dc.subjectNon and semiparametric methods-
dc.titleNonparametric regression M-quantiles-
dc.typeJournal Contribution-
dc.identifier.epage362-
dc.identifier.issue4-
dc.identifier.spage355-
dc.identifier.volume8-
dc.bibliographicCitation.oldjcat-
dc.identifier.doi10.1016/0167-7152(89)90044-8-
item.fulltextNo Fulltext-
item.contributorAntoch, J.-
item.contributorJANSSEN, Paul-
item.accessRightsClosed Access-
item.fullcitationAntoch, J. & JANSSEN, Paul (1989) Nonparametric regression M-quantiles. In: Statist. Prob. Letters, 8(4), p. 355-362.-
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