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High dimensional controlled variable selection with model-X knockoffs in the AFT model

Author

Listed:
  • Baihua He

    (University of Science and Technology of China)

  • Di Xia

    (Hubei University)

  • Yingli Pan

    (Hubei University)

Abstract

Interpretability and stability are two important characteristics required for the application of high dimensional data in statistics. Although the former has been favored by many existing forecasting methods to some extent, the latter in the sense of controlling the fraction of wrongly discovered features is still largely underdeveloped. Under the accelerated failure time model, this paper introduces a controlled variable selection method with the general framework of Model-X knockoffs to tackle high dimensional data. We provide theoretical justifications on the asymptotic false discovery rate (FDR) control. The proposed method has attracted significant interest due to its strong control of the FDR while preserving predictive power. Several simulation examples are conducted to assess the finite sample performance with desired interpretability and stability. A real data example from Acute Myeloid Leukemia study is analyzed to demonstrate the utility of the proposed method in practice.

Suggested Citation

  • Baihua He & Di Xia & Yingli Pan, 2024. "High dimensional controlled variable selection with model-X knockoffs in the AFT model," Computational Statistics, Springer, vol. 39(4), pages 1993-2009, June.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:4:d:10.1007_s00180-023-01426-5
    DOI: 10.1007/s00180-023-01426-5
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    References listed on IDEAS

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    1. Yingying Fan & Jinchi Lv & Mahrad Sharifvaghefi & Yoshimasa Uematsu, 2020. "IPAD: Stable Interpretable Forecasting with Knockoffs Inference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(532), pages 1822-1834, December.
    2. Ritesh Ramchandani & Dianne M. Finkelstein & David A. Schoenfeld, 2020. "Estimation for an accelerated failure time model with intermediate states as auxiliary information," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(1), pages 1-20, January.
    3. 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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