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Articles

Non-marginal feature screening for additive hazard model with ultrahigh-dimensional covariates

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Pages 1876-1894 | Received 07 Dec 2019, Accepted 11 May 2020, Published online: 17 Jun 2020
 

Abstract

Survival data with ultrahigh-dimensional covariates have been frequently encountered in medical studies and other fields. In this article, we propose a non-marginal feature screening procedure for the additive hazard model with ultrahigh-dimensional covariates. The proposed method utilizes the joint effects between covariates, which can effectively identify active covariates that are jointly dependent but marginally independent of the response. We develop an iterative hard-thresholding (IHT) algorithm to effectively implement the proposed procedure. Its convergence properties were investigated rigorously and further proved that the proposed procedure possesses the sure screening property. We conduct Monte Carlo simulation to evaluate the finite sample performance of the proposed procedure, and demonstrate the proposed procedure through an empirical analysis of a real data example.

Acknowledgements

The authors gratefully acknowledge helpful comments and suggestions from the editor and referees.

Additional information

Funding

This study is partially supported by the National Natural Science Foundation of China (grant No. 11871237).

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