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An adapted loss function for composite quantile regression with censored data

Author

Listed:
  • Xiaohui Yuan

    (Changchun University of Technology)

  • Xinran Zhang

    (Changchun University of Technology)

  • Wei Guo

    (Changchun University of Technology)

  • Qian Hu

    (Changchun University of Technology)

Abstract

This paper investigates an adapted loss function for the estimation of a linear regression with right censored responses. The adapted loss function could be used in composite quantile regression, which is a good method to handle the responses with high censored rate. Under some regular conditions, we establish the consistency and asymptotic normality of the resulting estimator. For estimation of regression parameters, we propose the MMCD algorithm, which generates satisfactory results for the proposed estimator. In addition, the algorithm can also be extended to the fused adaptive lasso penalized method to identify the interquantile commonality. The finite sample performances of the methods are further illustrated by numerical results and the analysis of two real datasets.

Suggested Citation

  • Xiaohui Yuan & Xinran Zhang & Wei Guo & Qian Hu, 2024. "An adapted loss function for composite quantile regression with censored data," Computational Statistics, Springer, vol. 39(3), pages 1371-1401, May.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:3:d:10.1007_s00180-023-01352-6
    DOI: 10.1007/s00180-023-01352-6
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