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Screening group variables in the proportional hazards model

Author

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  • Ahn, Kwang Woo
  • Sahr, Natasha
  • Kim, Soyoung

Abstract

We propose a method to screen group variables under the high dimensional group variable setting for the proportional hazards model. We study the sure screening property of the proposed method for independent and clustered survival data. The simulation study shows that the proposed method performs better for group variable screening than some existing procedures.

Suggested Citation

  • Ahn, Kwang Woo & Sahr, Natasha & Kim, Soyoung, 2018. "Screening group variables in the proportional hazards model," Statistics & Probability Letters, Elsevier, vol. 135(C), pages 20-25.
    • Handle: RePEc:eee:stapro:v:135:y:2018:i:c:p:20-25
    DOI: 10.1016/j.spl.2017.11.014
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    References listed on IDEAS

    as
    1. Zhao, Sihai Dave & Li, Yi, 2012. "Principled sure independence screening for Cox models with ultra-high-dimensional covariates," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 397-411.
    2. S. Wang & B. Nan & N. Zhu & J. Zhu, 2009. "Hierarchically penalized Cox regression with grouped variables," Biometrika, Biometrika Trust, vol. 96(2), pages 307-322.
    3. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
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    Cited by:

    1. Qu, Lianqiang & Wang, Xiaoyu & Sun, Liuquan, 2022. "Variable screening for varying coefficient models with ultrahigh-dimensional survival data," Computational Statistics & Data Analysis, Elsevier, vol. 172(C).

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