A Lack‐of‐Fit Test for Heteroscedastic Regression Models via Cosine‐Series Smoothers

Chin‐Shang Li

Australian & New Zealand Journal of Statistics, 2003, vol. 45, issue 4, 477-489

Abstract: In this paper, a test is derived to assess the validity of heteroscedastic nonlinear regression models by a non‐parametric cosine regression method. For order selection, the paper proposes a data‐driven method that uses the parametric null model optimal order. This method yields a test that is asymptotically normally distributed under the null hypothesis and is consistent against any fixed alternative. Simulation studies that test the lack of fit of a generalized linear model are conducted to compare the performance of the proposed test with that of an existing non‐parametric kernel test. A dataset of esterase levels is used to demonstrate the proposed method in practice.

Date: 2003
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