Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/7501
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Luan, Xin-Ze | en_US |
dc.contributor.author | Liang, Yong | en_US |
dc.contributor.author | Liu, Cheng | en_US |
dc.contributor.author | Prof. LEUNG Kwong Sak | en_US |
dc.contributor.author | Chan, Tak-Ming | en_US |
dc.contributor.author | Xu , Zong-Ben | en_US |
dc.contributor.author | Zhang, Hai | en_US |
dc.date.accessioned | 2023-03-16T03:44:52Z | - |
dc.date.available | 2023-03-16T03:44:52Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | Soft Computing, 2014, vol. 18, pp.143–152 | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.11861/7501 | - |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartof | Soft Computing | en_US |
dc.title | A novel L1/2 regularization shooting method for Cox’s proportional hazards model | en_US |
dc.type | Peer Reviewed Journal Article | en_US |
dc.identifier.doi | 10.1007/s00500-013-1042-6 | - |
item.fulltext | No Fulltext | - |
crisitem.author.dept | Department of Applied Data Science | - |
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