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Model pursuit and variable selection in the additive accelerated failure time model

Author

Listed:
  • Li Liu

    (Wuhan University)

  • Hao Wang

    (Wuhan University)

  • Yanyan Liu

    (Wuhan University)

  • Jian Huang

    (University of Iowa)

Abstract

In this paper, we propose a new semiparametric method to simultaneously select important variables, identify the model structure and estimate covariate effects in the additive AFT model, for which the dimension of covariates is allowed to increase with sample size. Instead of directly approximating the non-parametric effects as in most existing studies, we take a linear effect out to weak the condition required for model identifiability. To compute the proposed estimates numerically, we use an alternating direction method of multipliers algorithm so that it can be implemented easily and achieve fast convergence rate. Our method is proved to be selection consistent and possess an asymptotic oracle property. The performance of the proposed methods is illustrated through simulations and the real data analysis.

Suggested Citation

  • Li Liu & Hao Wang & Yanyan Liu & Jian Huang, 2021. "Model pursuit and variable selection in the additive accelerated failure time model," Statistical Papers, Springer, vol. 62(6), pages 2627-2659, December.
    • Handle: RePEc:spr:stpapr:v:62:y:2021:i:6:d:10.1007_s00362-020-01205-0
    DOI: 10.1007/s00362-020-01205-0
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    References listed on IDEAS

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    Cited by:

    1. Sumin Hou & Hao Lv, 2023. "A Group MCP Approach for Structure Identification in Non-Parametric Accelerated Failure Time Additive Regression Model," Mathematics, MDPI, vol. 11(22), pages 1-14, November.

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