Feature screening in ultrahigh-dimensional additive Cox model

Guangren Yang, Sumin Hou, Luheng Wang, Yanqing Sun

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


The additive Cox model is flexible and powerful for modelling the dynamic changes of regression coefficients in the survival analysis. This paper is concerned with feature screening for the additive Cox model with ultrahigh-dimensional covariates. The proposed screening procedure can effectively identify active predictors. That is, with probability tending to one, the selected variable set includes the actual active predictors. In order to carry out the proposed procedure, we propose an effective algorithm and establish the ascent property of the proposed algorithm. We further prove that the proposed procedure possesses the sure screening property. Furthermore, we examine the finite sample performance of the proposed procedure via Monte Carlo simulations, and illustrate the proposed procedure by a real data example.

Original languageEnglish (US)
Pages (from-to)1117-1133
Number of pages17
JournalJournal of Statistical Computation and Simulation
Issue number6
StatePublished - Apr 13 2018

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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