Bi-selection in the high-dimensional additive hazards regression model

Li Liu, Wen Su, Xingqiu Zhao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)


In this article, we consider a class of regularized regression under the additive hazards model with censored survival data and propose a novel approach to achieve simultaneous group selection, variable selection, and parameter estimation for high-dimensional censored data, by combining the composite penalty and the pseudoscore. We develop a local coordinate descent (LCD) algorithm for efficient computation and subsequently establish the theoretical properties for the proposed selection methods. As a result, the selectors possess both group selection oracle property and variable selection oracle property, and thus enable us to simultaneously identify important groups and important variables within selected groups with high probability. Simulation studies demonstrate that the proposed method and LCD algorithm perform well. A real data example is provided for illustra-tion.

Original languageEnglish
Pages (from-to)748-772
Number of pages25
JournalElectronic Journal of Statistics
Issue number1
Publication statusE-pub ahead of print - 21 Jan 2021


  • Additive hazards model
  • Composite penalty
  • High dimension
  • Local coordinate descent algorithm
  • Oracle property

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'Bi-selection in the high-dimensional additive hazards regression model'. Together they form a unique fingerprint.

Cite this