Regularized estimation in the accelerated failure time model with high-dimensional covariates

Jian Huang, Shuangge Ma, Huiliang Xie

Research output: Journal article publicationJournal articleAcademic researchpeer-review

128 Citations (Scopus)


We consider two regularization approaches, the LASSO and the threshold-gradient-directed regularization, for estimation and variable selection in the accelerated failure time model with multiple covariates based on Stute's weighted least squares method. The Stute estimator uses Kaplan-Meier weights to account for censoring in the least squares criterion. The weighted least squares objective function makes the adaptation of this approach to multiple covariate settings computationally feasible. We use V-fold cross-validation and a modified Akaike's Information Criterion for tuning parameter selection, and a bootstrap approach for variance estimation. The proposed method is evaluated using simulations and demonstrated on a real data example.
Original languageEnglish
Issue number3
Publication statusPublished - 1 Sept 2006
Externally publishedYes


  • Cross-validation
  • Threshold-gradient-directed regularization
  • Variable selection
  • Weighted least squares

ASJC Scopus subject areas

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


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