Testing lack of fit of regression models under heteroscedasticity | Zendy

Li ChinShang | Zendy

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Testing lack of fit of regression models under heteroscedasticity

Author(s) -

Li ChinShang

Publication year - 1999

Publication title -

canadian journal of statistics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.804

H-Index - 51

eISSN - 1708-945X

pISSN - 0319-5724

DOI - 10.2307/3316106

Subject(s) - heteroscedasticity , nonparametric regression , mathematics , null hypothesis , parametric statistics , nonparametric statistics , null distribution , kernel regression

A test is proposed for assessing the lack of fit of heteroscedastic nonlinear regression models that is based on comparison of nonparametric kernel and parametric fits. A data‐driven method is proposed for bandwidth selection using the asymptotically optimal bandwidth of the parametric null model which leads to a test that has a limiting normal distribution under the null hypothesis and is consistent against any fixed alternative. The resulting test is applied to the problem of testing the lack of fit of a generalized linear model.

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