Feature screening in ultrahigh-dimensional varying-coefficient Cox model

Guangren Yang, Ling Zhang, Runze Li, Yuan Huang

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


The varying-coefficient Cox model is flexible and useful for modeling the dynamic changes of regression coefficients in survival analysis. In this paper, we study feature screening for varying-coefficient Cox models in ultrahigh-dimensional covariates. The proposed screening procedure is based on the joint partial likelihood of all predictors, thus different from marginal screening procedures available in the literature. In order to carry out the new procedure, we propose an effective algorithm and establish its ascent property. We further prove that the proposed procedure possesses the sure screening property. That is, with probability tending to 1, the selected variable set includes the actual active predictors. We conducted simulations to evaluate the finite-sample performance of the proposed procedure and compared it with marginal screening procedures. A genomic data set is used for illustration purposes.

Original languageEnglish (US)
Pages (from-to)284-297
Number of pages14
JournalJournal of Multivariate Analysis
StatePublished - May 2019

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty


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