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

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  • Chin‐Shang Li

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.

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  • Chin‐Shang Li, 2003. "A Lack‐of‐Fit Test for Heteroscedastic Regression Models via Cosine‐Series Smoothers," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 45(4), pages 477-489, December.
    • Handle: RePEc:bla:anzsta:v:45:y:2003:i:4:p:477-489
    DOI: 10.1111/1467-842X.00299
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    Cited by:

    1. Chin-Shang Li, 2012. "Testing for no effect via splines," Computational Statistics, Springer, vol. 27(2), pages 343-357, June.

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