Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7505
Title: Adaptive L1/2 shooting regularization method for survival analysis using gene expression data Open Access
Authors: Liu, Xiao-Ying 
Liang, Yong 
Xu, Zong-Ben 
Zhang, Hai 
Prof. LEUNG Kwong Sak 
Issue Date: 2013
Source: The Scientific World Journal , 2013,475702
Journal: The Scientific World Journal 
Abstract: A new adaptive shooting regularization method for variable selection based on the Cox’s proportional hazards mode being proposed. This adaptive shooting algorithm can be easily obtained by the optimization of a reweighed iterative series of penalties and a shooting strategy of penalty. Simulation results based on high dimensional artificial data show that the adaptive shooting regularization method can be more accurate for variable selection than Lasso and adaptive Lasso methods. The results from real gene expression dataset (DLBCL) also indicate that the regularization method performs competitively.
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/7505
DOI: 10.1155/2013/475702
Appears in Collections:Applied Data Science - Publication

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