Additive inverse regression models with convolution-type operators

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dc.contributor.authorBissantz, Nicolai
dc.contributor.authorDette, Holger
dc.contributor.authorHildebrandt, Thimo
dc.date.accessioned2013-03-14T14:36:16Z
dc.date.available2013-03-14T14:36:16Z
dc.date.issued2013-03-14
dc.description.abstractIn a recent paper Birke and Bissantz (2008) considered the problem of nonparametric estimation in inverse regression models with convolution-type operators. For multivariate predictors nonparametric methods suff er from the curse of dimensionality and we consider inverse re- gression models with the additional qualitative assumption of additivity. In these models several additive estimators are studied. In particular, we investigate estimators under the random design assumption which are applicable when observations are not available on a grid. Finally, we compare this estimator with the marginal integration and the non-additive estimator by means of a simulation study. It is demonstrated that the new method yields a substantial improvement of the currently available procedures.en
dc.identifier.urihttp://hdl.handle.net/2003/30096
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-10346
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB 823;8/2013en
dc.subjectadditive modelsen
dc.subjectconvolution-type operatorsen
dc.subjectinverse regressionen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleAdditive inverse regression models with convolution-type operatorsen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access

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