Adaptive L1/2 shooting regularization method for survival analysis using gene expression data

A new adaptive L1/2 shooting regularization method for variable selection based on the Cox's proportional hazards mode being proposed. This adaptive L1/2 shooting algorithm can be easily obtained by the optimization of a reweighed iterative series of L1 penalties and a shooting strategy of L1/2 penalty. Simulation results based on high dimensional artificial data show that the adaptive L1/2 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 L1/2 regularization method performs competitively.