Regularization parameter selection in indirect regression by residual based bootstrap

Bissantz, Nicolai; Chown, Justin; Dette, Holger

Regularization parameter selection in indirect regression by residual based bootstrap

dc.contributor.author	Bissantz, Nicolai
dc.contributor.author	Chown, Justin
dc.contributor.author	Dette, Holger
dc.date.accessioned	2016-10-28T09:00:10Z
dc.date.available	2016-10-28T09:00:10Z
dc.date.issued	2016
dc.description.abstract	Residual-based analysis is generally considered a cornerstone of statistical methodology. For a special case of indirect regression, we investigate the residual-based empirical distribution function and provide a uniform expansion of this estimator, which is also shown to be asymptotically most precise. This investigation naturally leads to a completely data-driven technique for selecting a regularization parameter used in our indirect regression function estimator. The resulting methodology is based on a smooth bootstrap of the model residuals. A simulation study demonstrates the effectiveness of our approach.	de
dc.identifier.uri	http://hdl.handle.net/2003/35302
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-17345
dc.language.iso	en	de
dc.relation.ispartofseries	Discussion Paper / SFB823;56, 2016
dc.subject	bandwidth selection	de
dc.subject	smooth bootstrap	de
dc.subject	residual-based empirical distribution function	de
dc.subject	regularization	de
dc.subject	indirect nonparametric regression	de
dc.subject	deconvolution function estimator	de
dc.subject.ddc	310
dc.subject.ddc	330
dc.subject.ddc	620
dc.subject.rswk	Nichtparametrische Regression	de
dc.subject.rswk	Bootstrap-Statistik	de
dc.title	Regularization parameter selection in indirect regression by residual based bootstrap	de
dc.type	Text	de
dc.type.publicationtype	workingPaper	de
dcterms.accessRights	open access

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