Model checks in inverse regression models with convolution-type operators

dc.contributor.author	Bissantz, Nicolai	de
dc.contributor.author	Dette, Holger	de
dc.contributor.author	Proksch, Katharina	de
dc.date.accessioned	2009-10-29T10:28:54Z
dc.date.available	2009-10-29T10:28:54Z
dc.date.issued	2009-10-16	de
dc.description.abstract	We consider the problem of testing parametric assumptions in an inverse regression model with a convolution-type operator. An L^2-type goodness-of-fit test is proposed which compares the distance between a parametric and a nonparametric estimate of the regression function. Asymptotic normality of the corresponding test statistic is shown under the null hypothesis and under a general nonparametric alternative with different rates of convergence in both cases. The feasibility of the proposed test is demonstrated by means of a small simulation study. In particular, the power of the test against certain types of alternative is investigated.	en
dc.identifier.uri	http://hdl.handle.net/2003/26501
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-751
dc.language.iso	en	de
dc.relation.ispartofseries	Discussion Paper / SFB 823; 29/2009	de
dc.subject	goodness-of-fit test	en
dc.subject	imit theorems for quadratic forms	en
dc.subject	model selection	en
dc.subject	nverse problems	en
dc.subject.ddc	310	de
dc.subject.ddc	330	de
dc.subject.ddc	620	de
dc.title	Model checks in inverse regression models with convolution-type operators	en
dc.type	Text	de
dc.type.publicationtype	report	de
dcterms.accessRights	open access
eldorado.dnb.deposit	true

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