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New Inference Procedures for Semiparametric Varying‐Coefficient Partially Linear Cox Models

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  • Yunbei Ma
  • Xuan Luo

Abstract

In biomedical research, one major objective is to identify risk factors and study their risk impacts, as this identification can help clinicians to both properly make a decision and increase efficiency of treatments and resource allocation. A two‐step penalized‐based procedure is proposed to select linear regression coefficients for linear components and to identify significant nonparametric varying‐coefficient functions for semiparametric varying‐coefficient partially linear Cox models. It is shown that the penalized‐based resulting estimators of the linear regression coefficients are asymptotically normal and have oracle properties, and the resulting estimators of the varying‐coefficient functions have optimal convergence rates. A simulation study and an empirical example are presented for illustration.

Suggested Citation

  • Yunbei Ma & Xuan Luo, 2014. "New Inference Procedures for Semiparametric Varying‐Coefficient Partially Linear Cox Models," Journal of Applied Mathematics, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnljam:v:2014:y:2014:i:1:n:360249
    DOI: 10.1155/2014/360249
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    References listed on IDEAS

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    1. Wang, Lifeng & Li, Hongzhe & Huang, Jianhua Z., 2008. "Variable Selection in Nonparametric Varying-Coefficient Models for Analysis of Repeated Measurements," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1556-1569.
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    5. Johnson, Brent A. & Lin, D.Y. & Zeng, Donglin, 2008. "Penalized Estimating Functions and Variable Selection in Semiparametric Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 672-680, June.
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