Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/22550
Authors: 
Nagel, Eva-Renate
Dette, Holger
Neumeyer, Natalie
Year of Publication: 
2004
Series/Report no.: 
Technical Report / Universität Dortmund, SFB 475 Komplexitätsreduktion in Multivariaten Datenstrukturen 2004,38
Abstract: 
In 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.
Subjects: 
goodness-of-fit
residual process
parametric bootstrap
linear model
analysis of variance
M-estimation
nonparametric regression
Document Type: 
Working Paper

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