Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 485-496 |
| Number of pages | 12 |
| Journal | Canadian Journal of Statistics |
| Volume | 27 |
| Issue number | 3 |
| State | Published - Sep 1999 |
| Externally published | Yes |
Keywords
- Bandwidth selection
- Fit comparison test
- Kernel smoother
- Quasi-likelihood estimator
ASJC Scopus subject areas
- Statistics and Probability
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Cite this
Li, C-S. (1999). Testing lack of fit of regression models under heteroscedasticity. Canadian Journal of Statistics, 27(3), 485-496.