Assessing the adequacy of variance function in heteroscedastic regression models

Lan Wang, Xiao Hua Zhou

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Heteroscedastic data arise in many applications. In heteroscedastic regression analysis, the variance is often modeled as a parametric function of the covariates or the regression mean. We propose a kernel-smoothing type nonparametric test for checking the adequacy of a given parametric variance structure. The test does not need to specify a parametric distribution for the random errors. It is shown that the test statistic has an asymptotical normal distribution under the null hypothesis and is powerful against a large class of alternatives. We suggest a simple bootstrap algorithm to approximate the distribution of the test statistic in finite sample size. Numerical simulations demonstrate the satisfactory performance of the proposed test. We also illustrate the application by the analysis of a radioimmunoassay data set.

Original languageEnglish (US)
Pages (from-to)1218-1225
Number of pages8
Issue number4
StatePublished - Dec 2007
Externally publishedYes


  • Goodness-of-fit test
  • Heteroscedastic errors
  • Kernel smoothing
  • Pseudolikelihood
  • Variance function


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