Sufficient variable screening via directional regression with censored response

We in this paper propose a directional regression based approach for ultrahigh dimensional sufficient variable screening with censored responses. The new method is designed in a model-free manner and thus can be adapted to various complex model structures. Under some commonly used assumptions, we show that the proposed method enjoys the sure screening property when the dimension p diverges at an exponential rate of the sample size n. To improve the marginal screening method, the corresponding iterative screening algorithm and stability screening algorithm are further equipped. We demonstrate the effectiveness of the proposed method through simulation studies and a real data analysis.