A new test for the parametric form of the variance function in non-parametric regression. J. R. Stat. Soc. Ser. B-Stat. Methodol
AbstractIn the common non-parametric 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 that are obtained from the standardized non-parametric 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 Kolmogorov-Smimov and a Cramer-von Mises type of statistic for testing the parametric form of the conditional variance. The consistency of a bootstrap approximation is established, and the finite sample properties of this approximation 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.
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Bibliographic InfoPaper provided by Université catholique de Louvain in its series Open Access publications from Université catholique de Louvain with number info:hdl:2078.1/37210.
Date of creation: 2007
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Publication status: Published in Royal Statistical Society. Journal. Series B: Statistical Methodology (2007) v.69, p.903-917
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