Bootstrap Tests for the Error Distribution in Linear and Nonparametric Regression Models

dc.contributor.authorDette, Holgerde
dc.contributor.authorNagel, Eva-Renatede
dc.contributor.authorNeumeyer, Nataliede
dc.date.accessioned2004-12-06T18:39:28Z
dc.date.available2004-12-06T18:39:28Z
dc.date.issued2004de
dc.description.abstractIn this paper we investigate several tests for the hypothesis of a parametric form of the error distribution in the common linear and nonparametric regression model, which are based on empirical processes of residuals. It is well known that tests in this context are not asymptotically distribution-free and the parametric bootstrap is applied to deal with this problem. The performance of the resulting bootstrap test is investigated from an asymptotic point of view and by means of a simulation study. The results demonstrate that even for moderate sample sizes the parametric bootstrap provides a reliable and easy accessible solution to the problem of goodness-of-fit testing of assumptions regarding the error distribution in linear and nonparametric regression models.en
dc.format.extent255268 bytes
dc.format.extent462941 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.identifier.urihttp://hdl.handle.net/2003/4909
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-15022
dc.language.isoende
dc.publisherUniversitätsbibliothek Dortmundde
dc.subjectgoodness-of-fiten
dc.subjectresidual processen
dc.subjectparametric bootstrapen
dc.subjectlinear modelen
dc.subjectanalysis of varianceen
dc.subjectM-estimationen
dc.subjectnonparametric regressionen
dc.subject.ddc310de
dc.titleBootstrap Tests for the Error Distribution in Linear and Nonparametric Regression Modelsen
dc.typeTextde
dc.type.publicationtypereporten
dcterms.accessRightsopen access

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