Bootstrap tests for the error distribution in linear and nonparametric regression models
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. --
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Bibliographic InfoPaper provided by Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen in its series Technical Reports with number 2004,38.
Date of creation: 2004
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goodness-of-fit; residual process; parametric bootstrap; linear model; analysis of variance; M-estimation; nonparametric regression;
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Open Access publications from Tilburg University
urn:nbn:nl:ui:12-192434, Tilburg University.
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