Multiscale inference for multivariate deconvolution

dc.contributor.author	Eckle, Konstantin
dc.contributor.author	Bissantz, Nicolai
dc.contributor.author	Dette, Holger
dc.date.accessioned	2016-11-03T13:40:17Z
dc.date.available	2016-11-03T13:40:17Z
dc.date.issued	2016
dc.description.abstract	We propose multiscale tests for deconvolution in order to detect geometric features of an unknown multivariate density. Our approach uses simultaneous tests on all scales for the monotonicity of the density at arbitrary points in arbitrary directions. We consider the situation of polynomial decay of the Fourier transform of the error density in the de- convolution model (moderately ill-posed). We develop multiscale methods for identifying regions of monotonicity and a general procedure to detect the modes of a multivariate density. The theoretical results are illustrated by means of a simulation study.	en
dc.identifier.uri	http://hdl.handle.net/2003/35310
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-17353
dc.language.iso	en	de
dc.relation.ispartofseries	Discussion Paper / SFB823;62, 2016	en
dc.subject	multiple tests	en
dc.subject	X-ray astronomy	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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