Local constant and local bilinear multiple-output quantile regression

dc.contributor.author	Hallin, Marc
dc.contributor.author	Lu, Zudi
dc.contributor.author	Paindaveine, Davy
dc.contributor.author	Siman, Miroslav
dc.date.accessioned	2012-08-01T11:14:05Z
dc.date.available	2012-08-01T11:14:05Z
dc.date.issued	2012-08-01
dc.description.abstract	A new quantile regression concept, based on a directional version of Koenker and Bassett’s traditional single-output one, has been introduced in [Hallin, Paindaveine and ˇSiman, Annals of Statistics 2010, 635-703] for multiple-output regression problems. The polyhedral contours provided by the empirical counterpart of that concept, however, cannot adapt to nonlinear and/or heteroskedastic dependencies. This paper therefore introduces local constant and local linear versions of those contours, which both allow to asymptotically recover the conditional halfspace depth contours of the response. In the multiple-output context considered, the local linear construction actually is of a bilinear nature. Bahadur representation and asymptotic normality results are established. Illustrations are provided both on simulated and real data.	en
dc.identifier.uri	http://hdl.handle.net/2003/29570
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-4870
dc.language.iso	en	de
dc.relation.ispartofseries	Discussion Paper / SFB 823;32/2012	en
dc.subject.ddc	310
dc.subject.ddc	330
dc.subject.ddc	620
dc.title	Local constant and local bilinear multiple-output quantile regression	en
dc.type	Text	de
dc.type.publicationtype	workingPaper	de
dcterms.accessRights	open access

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