Novel Harmonic Regularization Approach for Variable Selection in Cox's Proportional Hazards Model

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 v...

Variable Selection for Cox's Proportional Hazards Model and Frailty Model

Variable Selection for Cox’s Proportional Hazards Model and Frailty Model By

A class of variable selection procedures for parametric models via nonconcave penalized likelihood was proposed in Fan and Li (2001a). It has been shown there that the resulting procedures perform as well as if the subset of significant variables were known in advance. Such a property is called an oracle property. The proposed procedures were illustrated in the context of linear regression, rob...

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 sho...

A proposal for variable selection in the Cox model

We propose a new method for variable selection and estimation in Cox's proportional hazards model. Our proposal minimizes the log partial likelihood subject to the sum of the absolute values of the parameters being bounded by a constant. Because of the nature of this constraint it tends to produce some coeecients that are exactly zero and hence gives interpretable models. The method is a variat...