Testing lack of fit of regression models under heteroscedasticity

Chin-Shang Li

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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 languageEnglish (US)
Pages (from-to)485-496
Number of pages12
JournalCanadian Journal of Statistics
Issue number3
StatePublished - Sep 1999
Externally publishedYes


  • Bandwidth selection
  • Fit comparison test
  • Kernel smoother
  • Quasi-likelihood estimator

ASJC Scopus subject areas

  • Statistics and Probability


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