Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/7501
Title: | A novel L1/2 regularization shooting method for Cox’s proportional hazards model |
Authors: | Luan, Xin-Ze Liang, Yong Liu, Cheng Prof. LEUNG Kwong Sak Chan, Tak-Ming Xu , Zong-Ben Zhang, Hai |
Issue Date: | 2014 |
Source: | Soft Computing, 2014, vol. 18, pp.143–152 |
Journal: | Soft Computing |
Abstract: | Nowadays, a series of methods are based on a L 1 penalty to solve the variable selection problem for a Cox’s proportional hazards model. In 2010, Xu et al. have proposed a L 1/2 regularization and proved that the L 1/2 penalty is sparser than the L 1 penalty in linear regression models. In this paper, we propose a novel shooting method for the L 1/2 regularization and apply it on the Cox model for variable selection. The experimental results based on comprehensive simulation studies, real Primary Biliary Cirrhosis and diffuse large B cell lymphoma datasets show that the L 1/2 regularization shooting method performs competitively. |
Type: | Peer Reviewed Journal Article |
URI: | http://hdl.handle.net/20.500.11861/7501 |
DOI: | 10.1007/s00500-013-1042-6 |
Appears in Collections: | Applied Data Science - Publication |
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