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

Xiaolin Chen, Catherine Chunling Liu and Sheng Xu ()
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Xiaolin Chen: Qufu Normal University
Catherine Chunling Liu: The Hong Kong Polytechnic University
Sheng Xu: The Hong Kong Polytechnic University

Computational Statistics, 2021, vol. 36, issue 2, No 5, 885-910

Abstract: 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.

Keywords: Cox’s model; LASSO initial; Locally Lipschitz optimization; Non-monotone proximal gradient; Joint feature screening (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s00180-020-01032-9

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