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An efficient algorithm for joint feature screening in ultrahigh-dimensional Cox’s model

Author

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  • Xiaolin Chen

    (Qufu Normal University)

  • Catherine Chunling Liu

    (The Hong Kong Polytechnic University)

  • Sheng Xu

    (The Hong Kong Polytechnic University)

Abstract

The Cox model is an exceedingly popular semiparametric hazard regression model for the analysis of time-to-event accompanied by explanatory variables. Within the ultrahigh-dimensional data setting, not like the marginal screening strategy, there is a joint feature screening method based on the partial likelihood of the Cox model but it leaves computational feasibility unsolved. In this paper, we develop an enhanced iterative hard-thresholding algorithm by adapting the non-monotone proximal gradient method under the Cox model. The proposed algorithm is efficient because it is computationally both effective and fast. Meanwhile, our proposed algorithm begins with a LASSO initial estimator rather than the naive zero initial and still enjoys sure screening in theory and further enhances the computational efficiency in practice. We also give a rigorous theory proof. The advantage of our proposed work is demonstrated by numerical studies and illustrated by the diffuse large B-cell lymphoma data example.

Suggested Citation

  • Xiaolin Chen & Catherine Chunling Liu & Sheng Xu, 2021. "An efficient algorithm for joint feature screening in ultrahigh-dimensional Cox’s model," Computational Statistics, Springer, vol. 36(2), pages 885-910, June.
  • Handle: RePEc:spr:compst:v:36:y:2021:i:2:d:10.1007_s00180-020-01032-9
    DOI: 10.1007/s00180-020-01032-9
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    References listed on IDEAS

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