Group selection in the Cox model with a diverging number of covariates

Jian Huang, Li Liu, Yanyan Liu, Xingqiu Zhao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

16 Citations (Scopus)

Abstract

In this article, we propose a variable selection approach in the Cox model when there is a group structure in a diverging number of covariates. Most of the existing variable selection methods are designed for either individual variable selection or group selection, but not for both. The proposed methods are capable of simultaneous group selection and individual variable selection within selected groups. Computational algorithms are developed for the proposed bi-level selection methods, and the properties of the proposed selection methods are established. The proposed group bridge penalized methods are able to correctly select the important groups and variables simultaneously with high probability in sparse models. Simulation studies indicate that the proposed methods work well and two examples are provided to illustrate the applications of the proposed methods to scientific problems.
Original languageEnglish
Pages (from-to)1787-1810
Number of pages24
JournalStatistica Sinica
Volume24
Issue number4
DOIs
Publication statusPublished - 1 Jan 2014

Keywords

  • Bi-level selection
  • Coordinate descent algorithm
  • Cox regression
  • Group bridge penalty
  • Survival data
  • Variable selection consistency

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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