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Statistics and Computing

, Volume 20, Issue 2, pp 165–176 | Cite as

Rank-based variable selection with censored data

  • Jinfeng Xu
  • Chenlei Leng
  • Zhiliang Ying
Article

Abstract

A rank-based variable selection procedure is developed for the semiparametric accelerated failure time model with censored observations where the penalized likelihood (partial likelihood) method is not directly applicable.

The new method penalizes the rank-based Gehan-type loss function with the 1 penalty. To correctly choose the tuning parameters, a novel likelihood-based χ 2-type criterion is proposed. Desirable properties of the estimator such as the oracle properties are established through the local quadratic expansion of the Gehan loss function.

In particular, our method can be easily implemented by the standard linear programming packages and hence numerically convenient. Extensions to marginal models for multivariate failure time are also considered. The performance of the new procedure is assessed through extensive simulation studies and illustrated with two real examples.

Keywords

Accelerated failure time model Adaptive Lasso BIC Gehan-type loss function Lasso Variable selection 

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of Statistics and Applied Probability, Risk Management InstituteNational University of SingaporeSingaporeSingapore
  2. 2.Department of StatisticsColumbia UniversityNew YorkUSA

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