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Shrinkage variable selection and estimation in proportional hazards models with additive structure and high dimensionality

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  • Lian, Heng
  • Li, Jianbo
  • Hu, Yuao

Abstract

Variable selection and estimation in proportional hazards models with additive relative risk is considered. Both objectives are achieved using a penalized partial likelihood with a group nonconcave penalty. Oracle properties of the estimator are demonstrated, when the dimensionality is allowed to be larger than sample size. To deal with the computational challenges when p>n, an active-set-type algorithm is proposed. Finally, the method is illustrated with simulation examples and a real microarray study.

Suggested Citation

  • Lian, Heng & Li, Jianbo & Hu, Yuao, 2013. "Shrinkage variable selection and estimation in proportional hazards models with additive structure and high dimensionality," Computational Statistics & Data Analysis, Elsevier, vol. 63(C), pages 99-112.
    • Handle: RePEc:eee:csdana:v:63:y:2013:i:c:p:99-112
    DOI: 10.1016/j.csda.2013.02.003
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    References listed on IDEAS

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    4. Hao Helen Zhang & Wenbin Lu, 2007. "Adaptive Lasso for Cox's proportional hazards model," Biometrika, Biometrika Trust, vol. 94(3), pages 691-703.
    5. Sun, Jie & Kopciuk, Karen A. & Lu, Xuewen, 2008. "Polynomial spline estimation of partially linear single-index proportional hazards regression models," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 176-188, September.
    6. Cai, Jianwen & Fan, Jianqing & Jiang, Jiancheng & Zhou, Haibo, 2007. "Partially Linear Hazard Regression for Multivariate Survival Data," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 538-551, June.
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    Cited by:

    1. Yang, Guangren & Zhang, Ling & Li, Runze & Huang, Yuan, 2019. "Feature screening in ultrahigh-dimensional varying-coefficient Cox model," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 284-297.

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