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dc.contributor.authorBirke, Melanie-
dc.contributor.authorBissantz, Nicolai-
dc.contributor.authorHolzmann, Hajo-
dc.description.abstractWe construct uniform confidence bands for the regression function in inverse, homoscedastic regression models with convolution-type operators. Here, the convolution is between two non-periodic functions on the whole real line rather than between two period functions on a compact interval, since the former situation arguably arises more often in applications. First, following Bickel and Rosenblatt [Ann. Statist. 1, 1071–1095] we construct asymptotic confidence bands which are based on strong approximations and on a limit theorem for the supremum of a stationary Gaussian process. Further, we propose bootstrap confidence bands based on the residual bootstrap. A simulation study shows that the bootstrap confidence bands perform reasonably well for moderate sample sizes. Finally, we apply our method to data from a gel electrophoresis experiment with genetically engineered neuronal receptor subunits incubated with rat brain extract.en
dc.subjectConfidence banden
dc.subjectInverse problemen
dc.subjectNonparametric regressionen
dc.subjectRate of convergenceen
dc.titleConfidence bands for inverse regression models with application to gel electrophoresisen
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 475

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