Sieve estimation of Cox models with latent structures

Yongxiu Cao, Jian Huang, Yanyan Liu, Xingqiu Zhao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)

Abstract

We propose a semiparametric pursuit method to simultaneously identify and estimate linear and nonparametric covariate effects based on B-spline expansions through a penalized group selection method with concave penalties. We show that the estimators of the linear effects and the nonparametric component are consistent. Furthermore, we establish the asymptotic normality of the estimator of the linear effects. To compute the proposed estimators, we develop a modified blockwise majorization descent algorithm that is efficient and easy to implement. Simulation studies demonstrate that the proposed method performs well in finite sample situations. We also use the primary biliary cirrhosis data to illustrate its application.
Original languageEnglish
Pages (from-to)1086-1097
Number of pages12
JournalBiometrics
Volume72
Issue number4
DOIs
Publication statusPublished - 1 Dec 2016

Keywords

  • Group selection
  • Model-pursuit consistency
  • Modified blockwise majorization descent algorithm
  • Partially linear Cox model
  • Penalized partial likelihood

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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