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Robust empirical likelihood for partially linear models via weighted composite quantile regression

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  • Peixin Zhao

    (Chongqing Technology and Business University)

  • Xiaoshuang Zhou

    (Dezhou University)

Abstract

In this paper, we investigate robust empirical likelihood inferences for partially linear models. Based on weighted composite quantile regression and QR decomposition technology, we propose a new estimation method for the parametric components. Under some regularity conditions, we prove that the proposed empirical log-likelihood ratio is asymptotically chi-squared, and then the confidence intervals for the parametric components are constructed. The resulting estimators for parametric components are not affected by the nonparametric components, and then it is more robust, and is easy for application in practice. Some simulations analysis and a real data application are conducted for further illustrating the performance of the proposed method.

Suggested Citation

  • Peixin Zhao & Xiaoshuang Zhou, 2018. "Robust empirical likelihood for partially linear models via weighted composite quantile regression," Computational Statistics, Springer, vol. 33(2), pages 659-674, June.
    • Handle: RePEc:spr:compst:v:33:y:2018:i:2:d:10.1007_s00180-018-0793-z
    DOI: 10.1007/s00180-018-0793-z
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    References listed on IDEAS

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    Cited by:

    1. Xiaoshuang Zhou & Peixin Zhao & Yujie Gai, 2022. "Imputation-based empirical likelihood inferences for partially nonlinear quantile regression models with missing responses," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(4), pages 705-722, December.

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