Variable selection and structure estimation for ultrahigh-dimensional additive hazards models

Li Liu, Yanyan Liu, Feng Su, Xingqiu Zhao

Research output: Journal article publicationJournal articleAcademic researchpeer-review


We develop a class of regularization methods based on the penalized sieve least squares for simultaneous model pursuit, variable selection, and estimation in high-dimensional additive hazards regression models. In the framework of sparse ultrahigh-dimensional models, the asymptotic properties of the proposed estimators include structure identification consistency and oracle variable selection. The computational process can be efficiently implemented by applying the blockwise majorization descent algorithm. Simulation studies demonstrate the performance of the proposed methodology, and the primary biliary cirrhosis data analysis is provided for illustration.

Original languageEnglish
Pages (from-to)826-852
Number of pages27
JournalCanadian Journal of Statistics
Issue number3
Early online date21 Jan 2021
Publication statusPublished - Sep 2021


  • Additive hazards regression
  • model pursuit
  • penalized sieve least squares
  • ultrahigh-dimensional censored data
  • variable selection

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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