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Smoothing Estimation of Parameters in Censored Quantile Linear Regression Model

Author

Listed:
  • Mingquan Wang

    (School of Mathematical Sciences, Nanjing Normal University, Nanjing 210023, China)

  • Xiaohua Ma

    (School of Mathematical Sciences, Nanjing Normal University, Nanjing 210023, China)

  • Xinrui Wang

    (College of International Languages and Cultures, Hohai University, Nanjing 211100, China)

  • Jun Wang

    (School of Mathematical Sciences, Nanjing Normal University, Nanjing 210023, China)

  • Xiuqing Zhou

    (School of Mathematical Sciences, Nanjing Normal University, Nanjing 210023, China)

  • Qibing Gao

    (School of Mathematical Sciences, Nanjing Normal University, Nanjing 210023, China)

Abstract

In this paper, we propose a smoothing estimation method for censored quantile regression models. The method associates the convolutional smoothing estimation with the loss function, which is quadratically derivable and globally convex by using a non-negative kernel function. Thus, the parameters of the regression model can be computed by using the gradient-based iterative algorithm. We demonstrate the convergence speed and asymptotic properties of the smoothing estimation for large samples in high dimensions. Numerical simulations show that the smoothing estimation method for censored quantile regression models improves the estimation accuracy, computational speed, and robustness over the classical parameter estimation method. The simulation results also show that the parametric methods perform better than the KM method in estimating the distribution function of the censored variables. Even if there is an error setting in the distribution estimation, the smoothing estimation does not fluctuate too much.

Suggested Citation

  • Mingquan Wang & Xiaohua Ma & Xinrui Wang & Jun Wang & Xiuqing Zhou & Qibing Gao, 2025. "Smoothing Estimation of Parameters in Censored Quantile Linear Regression Model," Mathematics, MDPI, vol. 13(2), pages 1-28, January.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:2:p:192-:d:1562846
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    References listed on IDEAS

    as
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