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Data Augmentation Based Quantile Regression Estimation for Censored Partially Linear Additive Model

Author

Listed:
  • Lu Li

    (Zhejiang Gongshang University)

  • Ruiting Hao

    (Zhejiang Gongshang University)

  • Xiaorong Yang

    (Zhejiang Gongshang University)

Abstract

As a common semiparametric mode, the partially linear additive model has flexible structures, and it has been widely used in practice. In this paper, we study the quantile regression estimation of the model when its responses are censored. In particular, we consider a more general censoring framework, which allows different types of censoring (left, right, double, or interval censoring) simultaneously. Based on the general principles of data augmentation, we propose a unified iterative algorithm, where the censored data is imputed by sampling data from the quantile process, and regression parameters are refitted by using bootstrap samples. Monte Carlo simulations are conducted to verify the finite-sample properties of the method, and the results show its good performance. The application to Boston housing price data further illustrates the advantages of this method in practice.

Suggested Citation

  • Lu Li & Ruiting Hao & Xiaorong Yang, 2024. "Data Augmentation Based Quantile Regression Estimation for Censored Partially Linear Additive Model," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 1083-1112, August.
    • Handle: RePEc:kap:compec:v:64:y:2024:i:2:d:10.1007_s10614-023-10473-5
    DOI: 10.1007/s10614-023-10473-5
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    References listed on IDEAS

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