Liu, ChengChengLiuLiang, YongYongLiangLuan, Xin-ZeXin-ZeLuanProf. LEUNG Kwong SakChan, Tak-MingTak-MingChanXu, Zong-BenZong-BenXuZhang, HaiHaiZhang2023-03-162023-03-162014Applied Soft Computing, January 2014, Vol. 14, Part C, pp. 498-5031568-49461872-9681http://hdl.handle.net/20.500.11861/7502In this paper, we investigate to use the L1/2 regularization method for variable selection based on the Cox's proportional hazards model. The L1/2 regularization can be taken as a representative of Lq (0 < q < 1) regularizations and has been demonstrated many attractive properties. To solve the L1/2 penalized Cox model, we propose a coordinate descent algorithm with a new univariate half thresholding operator which is applicable to high-dimensional biological data. Simulation results based on standard artificial data show that the L1/2 regularization method can be more accurate for variable selection than Lasso and SCAD methods. The results from real DNA microarray datasets indicate the L1/2 regularization method performs competitively.enPeer Reviewed Journal Article10.1016/j.asoc.2013.09.006