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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