A Bayesian Approach to Sparse Cox Regression in High-Dimentional Survival Analysis

摘要

Survival prediction and prognostic factor identification play an important role in machine learning research. This paper employs the machine learning regression algorithms for constructing survival model. The paper suggests a new Bayesian framework for feature selection in high-dimensional Cox regression problems. The proposed approach gives a strong probabilistic statement of the shrinkage criterion for feature selection. The proposed regularization gives the estimates that are unbiased, possesses grouping and oracle properties, their maximal risk diverges to a finite value. Experimental results show that the proposed framework is competitive on both simulated data and publicly available real data sets.