Goodness-of-fit testing the error distribution in multivariate indirect regression

dc.contributor.author	Chown, Justin
dc.contributor.author	Bissantz, Nicolai
dc.contributor.author	Dette, Holger
dc.date.accessioned	2018-12-14T14:03:05Z
dc.date.available	2018-12-14T14:03:05Z
dc.date.issued	2018
dc.description.abstract	We propose a goodness-of-fit test for the distribution of errors from a multivariate indirect regression model. The test statistic is based on the Khmaladze transformation of the empirical process of standardized residuals. This goodness-of-fit test is consistent at the root-n rate of convergence, and the test can maintain power against local alternatives converging to the null at a root-n rate.	en
dc.identifier.uri	http://hdl.handle.net/2003/37835
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-19830
dc.language.iso	en	de
dc.relation.ispartofseries	Discussion Paper / SFB823;34/2018	en
dc.subject	hypothesis testing	en
dc.subject	regularization	en
dc.subject	multivariate regression	en
dc.subject	inverse problems	en
dc.subject	indirect regression estimator	en
dc.subject.ddc	310
dc.subject.ddc	330
dc.subject.ddc	620
dc.title	Goodness-of-fit testing the error distribution in multivariate indirect regression	en
dc.type	Text	de
dc.type.publicationtype	workingPaper	de
dcterms.accessRights	open access
eldorado.dnb.deposit	true	de
eldorado.secondarypublication	false	de

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