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Iterative L1/2 Regularization Algorithm for Variable Selection in the Cox Proportional Hazards Model

  • Cheng Liu
  • Yong Liang
  • Xin-Ze Luan
  • Kwong-Sak Leung
  • Tak-Ming Chan
  • Zong-Ben Xu
  • Hai Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7332)

Abstract

In this paper, we investigate to use theL 1/2 regularization method for variable selection based on the Cox’s proportional hazards model. The L 1/2 regularization method isa reweighed iterative algorithm with the adaptively weighted L 1 penalty on regression coefficients. The algorithm of theL 1/2 regularization method can be easily obtained by a series of L 1 penalties. Simulation results based on standard artificial data show that theL 1/2 regularization method can be more accurate for variable selection than Lasso and adaptive Lasso methods. The results from Primary Biliary Cirrhosis (PBC) dataset indicate theL 1/2 regularization method performs competitively.

Keywords

Lasso L1/2 regularization Variable selection Cox model 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Cheng Liu
    • 1
  • Yong Liang
    • 1
  • Xin-Ze Luan
    • 1
  • Kwong-Sak Leung
    • 2
  • Tak-Ming Chan
    • 2
  • Zong-Ben Xu
    • 3
  • Hai Zhang
    • 3
  1. 1.Macau University of Science and TechnologyChina
  2. 2.Chinese University of Hong KongHong Kong
  3. 3.Xi’an Jiaotong UniversityXi’anChina

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