Multiscale inference for multivariate deconvolution

dc.contributor.author	Eckle, Konstantin
dc.contributor.author	Bissantz, Nicolai
dc.contributor.author	Dette, Holger
dc.date.accessioned	2016-11-24T15:30:35Z
dc.date.available	2016-11-24T15:30:35Z
dc.date.issued	2016
dc.description.abstract	In this paper we provide new methodology for inference of the geometric features of a multivariate density in deconvolution. Our approach is based on multiscale tests to detect significant directional derivatives of the unknown density at arbitrary points in arbitrary directions. The multiscale method is used to identify regions of monotonicity and to construct a general procedure for the detection of modes of the multivariate density. Moreover, as an important application a significance test for the presence of a local maximum at a pre-specified point is proposed. The performance of the new methods is investigated from a theoretical point of view and the finite sample properties are illustrated by means of a small simulation study.	en
dc.identifier.uri	http://hdl.handle.net/2003/35626
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-17667
dc.language.iso	en	de
dc.relation.ispartofseries	Discussion Paper / SFB823;79, 2016	en
dc.subject	deconvolution	en
dc.subject	Gaussian approximation	en
dc.subject	multiple tests	en
dc.subject	multivariate density	en
dc.subject	modes	en
dc.subject.ddc	310
dc.subject.ddc	330
dc.subject.ddc	620
dc.title	Multiscale inference for multivariate deconvolution	en
dc.type	Text	de
dc.type.publicationtype	workingPaper	de
dcterms.accessRights	open access

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