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Imputation based statistical inference for partially linear quantile regression models with missing responses

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  • Peixin Zhao

    (Chongqing Technology and Business University)

  • Xinrong Tang

    (Chongqing Technology and Business University)

Abstract

In this paper, we consider the confidence interval construction for partially linear quantile regression models with missing response at random. We propose an imputation based empirical likelihood method to construct confidence intervals for the parametric components and the nonparametric components, and show that the proposed empirical log-likelihood ratios are both asymptotically Chi-squared in theory. Then, the confidence region for the parametric component and the pointwise confidence interval for the nonparametric component are constructed. Some simulation studies and a real data application are carried out to assess the performance of the proposed estimation method, and simulation results indicate that the proposed method is workable.

Suggested Citation

  • Peixin Zhao & Xinrong Tang, 2016. "Imputation based statistical inference for partially linear quantile regression models with missing responses," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(8), pages 991-1009, November.
    • Handle: RePEc:spr:metrik:v:79:y:2016:i:8:d:10.1007_s00184-016-0586-8
    DOI: 10.1007/s00184-016-0586-8
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    References listed on IDEAS

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    Cited by:

    1. Shuanghua Luo & Yuxin Yan & Cheng-yi Zhang, 2024. "Two-Stage Estimation of Partially Linear Varying Coefficient Quantile Regression Model with Missing Data," Mathematics, MDPI, vol. 12(4), pages 1-15, February.
    2. Chang-Sheng Liu & Han-Ying Liang, 2023. "Bayesian empirical likelihood of quantile regression with missing observations," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(3), pages 285-313, April.
    3. 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.

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