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Focused information criterion and model averaging in censored quantile regression

Author

Listed:
  • Jiang Du

    (Beijing University of Technology
    Collaborative Innovation Center on Capital Social Construction and Social Management)

  • Zhongzhan Zhang

    (Beijing University of Technology
    Collaborative Innovation Center on Capital Social Construction and Social Management)

  • Tianfa Xie

    (Beijing University of Technology
    Collaborative Innovation Center on Capital Social Construction and Social Management)

Abstract

In this paper, we study model selection and model averaging for quantile regression with randomly right censored response. We consider a semi-parametric censored quantile regression model without distribution assumptions. Under general conditions, a focused information criterion and a frequentist model averaging estimator are proposed, and theoretical properties of the proposed methods are established. The performances of the procedures are illustrated by extensive simulations and the primary biliary cirrhosis data.

Suggested Citation

  • Jiang Du & Zhongzhan Zhang & Tianfa Xie, 2017. "Focused information criterion and model averaging in censored quantile regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(5), pages 547-570, July.
    • Handle: RePEc:spr:metrik:v:80:y:2017:i:5:d:10.1007_s00184-017-0616-1
    DOI: 10.1007/s00184-017-0616-1
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    References listed on IDEAS

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    Cited by:

    1. Guozhi Hu & Weihu Cheng & Jie Zeng, 2023. "Optimal Model Averaging for Semiparametric Partially Linear Models with Censored Data," Mathematics, MDPI, vol. 11(3), pages 1-21, February.

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