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Sieve estimation of Cox models with latent structures

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  • Yongxiu Cao
  • Jian Huang
  • Yanyan Liu
  • Xingqiu Zhao

Abstract

This article considers sieve estimation in the Cox model with an unknown regression structure based on right‐censored data. We propose a semiparametric pursuit method to simultaneously identify and estimate linear and nonparametric covariate effects based on B‐spline expansions through a penalized group selection method with concave penalties. We show that the estimators of the linear effects and the nonparametric component are consistent. Furthermore, we establish the asymptotic normality of the estimator of the linear effects. To compute the proposed estimators, we develop a modified blockwise majorization descent algorithm that is efficient and easy to implement. Simulation studies demonstrate that the proposed method performs well in finite sample situations. We also use the primary biliary cirrhosis data to illustrate its application.

Suggested Citation

  • Yongxiu Cao & Jian Huang & Yanyan Liu & Xingqiu Zhao, 2016. "Sieve estimation of Cox models with latent structures," Biometrics, The International Biometric Society, vol. 72(4), pages 1086-1097, December.
    • Handle: RePEc:bla:biomet:v:72:y:2016:i:4:p:1086-1097
    DOI: 10.1111/biom.12529
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    References listed on IDEAS

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    Cited by:

    1. Jialiang Li & Tonghui Yu & Jing Lv & Mei‐Ling Ting Lee, 2021. "Semiparametric model averaging prediction for lifetime data via hazards regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1187-1209, November.
    2. 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.
    3. Yang, Yanlin & Hu, Xuemei & Jiang, Huifeng, 2022. "Group penalized logistic regressions predict up and down trends for stock prices," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).

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