Smooth backfitting in additive inverse regression

dc.contributor.author	Bissantz, Nicolai
dc.contributor.author	Dette, Holger
dc.contributor.author	Hildebrandt, Thimo
dc.date.accessioned	2013-10-11T07:26:30Z
dc.date.available	2013-10-11T07:26:30Z
dc.date.issued	2013-10-11
dc.description.abstract	We consider the problem of estimating an additive regression function in an inverse regression model with a convolution type operator. A smooth back fitting procedure is developed and asymptotic normality of the resulting estimator is established. Compared to other methods for the estimation in additive models the new approach neither requires observations on a regular grid nor the estimation of the joint density of the predictor. It is also demonstrated by means of a simulation study that the backfitting estimator outperforms the marginal integration method at least by a factor two with respect to the integrated mean squared error criterion.	en
dc.identifier.uri	http://hdl.handle.net/2003/31093
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-5618
dc.language.iso	en	de
dc.relation.ispartofseries	Discussion Paper / SFB 823;37/2013
dc.subject	additive models	en
dc.subject	curse of dimensionality	en
dc.subject	inverse regression	en
dc.subject	smooth back tting	en
dc.subject.ddc	310
dc.subject.ddc	330
dc.subject.ddc	620
dc.title	Smooth backfitting in additive inverse regression	en
dc.type	Text	de
dc.type.publicationtype	workingPaper	de
dcterms.accessRights	open access

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