Authors: Dette, Holger
Van Keilegom, Ingrid
Title: A new test for the parametric form of the variance function in nonparametric regression
Language (ISO): en
Abstract: In the common nonparametric regression model the problem of testing for the parametric form of the conditional variance is considered. A stochastic process based on the difference between the empirical processes obtained from the standardized nonparametric residuals under the null hypothesis (of a specific parametric form of the variance function) and the alternative is introduced and its weak convergence established. This result is used for the construction of a Cramér von Mises type statistic for testing the parametric form of the conditional variance. The finite sample properties of a bootstrap version of this test are investigated by means of a simulation study. In particular the new procedure is compared with some of the currently available methods for this problem and its performance is illustrated by means of a data example.
Subject Headings: Bootstrap
Kernel estimation
Nonparametric regression
Residual distribution
Testing heteroscedasticity
Testing homoscedasticity
Issue Date: 2005-10-11T14:37:10Z
Appears in Collections:Sonderforschungsbereich (SFB) 475

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