Variable selection for multivariate failure time data

Jianwen Cai, Jianqing Fan, Runze Li, Haibo Zhou

Research output: Contribution to journalArticlepeer-review

60 Scopus citations

Abstract

In this paper, we propose a penalised pseudo-partial likelihood method for variable selection with multivariate failure time data with a growing number of regression coefficients. Under certain regularity conditions, we show the consistency and asymptotic normality of the penalised likelihood estimators. We further demonstrate that, for certain penalty functions with proper choices of regularisation parameters, the resulting estimator can correctly identify the true model, as if it were known in advance. Based on a simple approximation of the penalty function, the proposed method can be easily carried out with the Newton-Raphson algorithm. We conduct extensive Monte Carlo simulation studies to assess the finite sample performance of the proposed procedures. We illustrate the proposed method by analysing a dataset from the Framingham Heart Study.

Original languageEnglish (US)
Pages (from-to)303-316
Number of pages14
JournalBiometrika
Volume92
Issue number2
DOIs
StatePublished - Jun 2005

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Keywords

  • Cox's model
  • Marginal hazards model
  • Penalised likelihood
  • Smoothly clipped absolute deviation
  • Variable selection

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