Heteroscedasticity testing for regression models: A dimension reduction-based model adaptive approach

Xuehu Zhu, Fei Chen, Xu Guo, Lixing Zhu*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

16 Citations (Scopus)


Heteroscedasticity testing is of importance in regression analysis. Existing local smoothing tests suffer severely from curse of dimensionality even when the number of covariates is moderate because of use of nonparametric estimation. A dimension reduction-based model adaptive test is proposed which behaves like a local smoothing test as if the number of covariates was equal to the number of their linear combinations in the mean regression function, in particular, equal to 1 when the mean function contains a single index. The test statistic is asymptotically normal under the null hypothesis such that critical values are easily determined. The finite sample performances of the test are examined by simulations and a real data analysis.

Original languageEnglish
Pages (from-to)263-283
Number of pages21
JournalComputational Statistics and Data Analysis
Publication statusPublished - Nov 2016

Scopus Subject Areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

User-Defined Keywords

  • Heteroscedasticity testing
  • Model-adaption
  • Sufficient dimension reduction


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