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Two-Stage Estimation of Partially Linear Varying Coefficient Quantile Regression Model with Missing Data

Author

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  • Shuanghua Luo

    (School of Science, Xi’an Polytechnic University, Xi’an 710048, China)

  • Yuxin Yan

    (School of Science, Xi’an Polytechnic University, Xi’an 710048, China)

  • Cheng-yi Zhang

    (School of Economics and Finance, Xi’an Jiaotong University, Xi’an 710061, China)

Abstract

In this paper, the statistical inference of the partially linear varying coefficient quantile regression model is studied under random missing responses. A two-stage estimation procedure is developed to estimate the parametric and nonparametric components involved in the model. Furthermore, the asymptotic properties of the estimators obtained are established under some mild regularity conditions. In addition, the empirical log-likelihood ratio statistic based on imputation is proposed, and it is proven that this statistic obeys the standard Chi-square distribution; thus, the empirical likelihood confidence interval of the parameter component of the model is constructed. Finally, simulation results show that the proposed estimation method is feasible and effective.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:4:p:578-:d:1338875
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    References listed on IDEAS

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    3. Liugen Xue & Lixing Zhu, 2007. "Empirical Likelihood Semiparametric Regression Analysis for Longitudinal Data," Biometrika, Biometrika Trust, vol. 94(4), pages 921-937.
    4. Zhou, Xian & You, Jinhong, 2004. "Wavelet estimation in varying-coefficient partially linear regression models," Statistics & Probability Letters, Elsevier, vol. 68(1), pages 91-104, June.
    5. 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.
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