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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