Iterative L1/2 regularization algorithm for variable selection in the Cox proportional hazards model

Abstract

In this paper, we investigate to use theL1/2 regularization method for variable selection based on the Cox's proportional hazards model. The L1/2 regularization method isa reweighed iterative algorithm with the adaptively weighted L1 penalty on regression coefficients. The algorithm of the L1/2 regularization method can be easily obtained by a series of L1 penalties. Simulation results based on standard artificial data show that the L1/2 regularization method can be more accurate for variable selection than Lasso and adaptive Lasso methods. The results from Primary Biliary Cirrhosis (PBC) dataset indicate the L 1/2 regularization method performs competitively. © 2012 Springer-Verlag.