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

Xiaolin Chen, Catherine Chunling Liu, Sheng Xu

Research output: Journal article publicationJournal articleAcademic researchpeer-review


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.

Original languageEnglish
Pages (from-to)885-910
Number of pages26
JournalComputational Statistics
Issue number2
Publication statusPublished - Jun 2021


  • Cox’s model
  • Joint feature screening
  • LASSO initial
  • Locally Lipschitz optimization
  • Non-monotone proximal gradient

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Mathematics

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