array(2) { ["lab"]=> string(4) "1409" ["publication"]=> string(5) "12511" } Iterative l1/2 regularization algorithm for variable selection in the cox proportional hazards model - Liang Yong | LabXing

Liang Yong

简介 中藥質量控制資訊系統，生物資訊學

大学 Macau University of Science and Technology / Faculty of Information Technology
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Iterative l1/2 regularization algorithm for variable selection in the cox proportional hazards model

2012
会议 International Conference in Swarm Intelligence
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.

  • 页码 11-17
  • Springer, Berlin, Heidelberg
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