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

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.