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Model averaging for right censored data with measurement error

Author

Listed:
  • Zhongqi Liang

    (Hangzhou City University
    Zhejiang University)

  • Caiya Zhang

    (Hangzhou City University)

  • Linjun Xu

    (Zhejiang Gongshang University)

Abstract

This paper studies a novel model averaging estimation issue for linear regression models when the responses are right censored and the covariates are measured with error. A novel weighted Mallows-type criterion is proposed for the considered issue by introducing multiple candidate models. The weight vector for model averaging is selected by minimizing the proposed criterion. Under some regularity conditions, the asymptotic optimality of the selected weight vector is established in terms of its ability to achieve the lowest squared loss asymptotically. Simulation results show that the proposed method is superior to the other existing related methods. A real data example is provided to supplement the actual performance.

Suggested Citation

  • Zhongqi Liang & Caiya Zhang & Linjun Xu, 2024. "Model averaging for right censored data with measurement error," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 30(2), pages 501-527, April.
    • Handle: RePEc:spr:lifeda:v:30:y:2024:i:2:d:10.1007_s10985-024-09620-3
    DOI: 10.1007/s10985-024-09620-3
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