A novel L1/2 regularization shooting method for Cox's proportional hazards model

Nowadays, a series of methods are based on a L1 penalty to solve the variable selection problem for a Cox’s proportional hazards model. In 2010, Xu et al. have proposed a L1/2 regularization and proved that the L1/2 penalty is sparser than the L1 penalty in linear regression models. In this paper, we propose a novel shooting method for the L1/2 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 L1/2 regularization shooting method performs competitively.