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Reproducible feature selection in high-dimensional accelerated failure time models

Author

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  • Dong, Yan
  • Li, Daoji
  • Zheng, Zemin
  • Zhou, Jia

Abstract

We propose a new feature selection procedure with guaranteed FDR control for high-dimensional AFT models, which is among the first attempts of reproducible learning in survival analysis. The effectiveness of the proposed method is theoretically and numerically demonstrated.

Suggested Citation

  • Dong, Yan & Li, Daoji & Zheng, Zemin & Zhou, Jia, 2022. "Reproducible feature selection in high-dimensional accelerated failure time models," Statistics & Probability Letters, Elsevier, vol. 181(C).
    • Handle: RePEc:eee:stapro:v:181:y:2022:i:c:s0167715221002376
    DOI: 10.1016/j.spl.2021.109275
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    References listed on IDEAS

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    1. Stute, W., 1993. "Consistent Estimation Under Random Censorship When Covariables Are Present," Journal of Multivariate Analysis, Elsevier, vol. 45(1), pages 89-103, April.
    2. Jian Huang & Shuangge Ma & Huiliang Xie, 2006. "Regularized Estimation in the Accelerated Failure Time Model with High-Dimensional Covariates," Biometrics, The International Biometric Society, vol. 62(3), pages 813-820, September.
    3. Yingying Fan & Emre Demirkaya & Gaorong Li & Jinchi Lv, 2020. "RANK: Large-Scale Inference With Graphical Nonlinear Knockoffs," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 362-379, January.
    4. Anders Gorst-Rasmussen & Thomas Scheike, 2013. "Independent screening for single-index hazard rate models with ultrahigh dimensional features," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(2), pages 217-246, March.
    5. Emmanuel Candès & Yingying Fan & Lucas Janson & Jinchi Lv, 2018. "Panning for gold: ‘model‐X’ knockoffs for high dimensional controlled variable selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(3), pages 551-577, June.
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