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Testing heteroscedasticity in partially linear models with missing covariates

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  • Xiaohui Liu
  • Zhizhong Wang
  • Xuemei Hu

Abstract

The purpose of this paper is to investigate the underlying heteroscedasticity in a partially linear model with missing covariates by using the empirical likelihood method. Two new test statistics are proposed based on the inverse probability-weighted idea. Under the null hypothesis, the resulting test statistics are shown to have standard chi-squared distributions asymptotically. Simulation studies show that the proposed statistics behave well. An example of an AIDS clinical trial data set is also used for illustrating our methods.

Suggested Citation

  • Xiaohui Liu & Zhizhong Wang & Xuemei Hu, 2011. "Testing heteroscedasticity in partially linear models with missing covariates," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(2), pages 321-337.
    • Handle: RePEc:taf:gnstxx:v:23:y:2011:i:2:p:321-337
    DOI: 10.1080/10485252.2010.515306
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    References listed on IDEAS

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    Cited by:

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    2. Zhangong Zhou & Linjun Tang, 2019. "Testing for parametric component of partially linear models with missing covariates," Statistical Papers, Springer, vol. 60(3), pages 747-760, June.

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