A new test for the parametric form of the variance function in non-parametric regression

55Citations
Citations of this article
11Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

In 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-Smirnov and a Cramér-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. © 2007 Royal Statistical Society.

Cite

CITATION STYLE

APA

Dette, H., Neumeyer, N., & Van Keilegom, I. (2007). A new test for the parametric form of the variance function in non-parametric regression. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 69(5), 903–917. https://doi.org/10.1111/j.1467-9868.2007.00616.x

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free