摘要：The varying coefficient model has become a very popular statistical tool for describing the dynamic effects of covariates on the response. In this article, we develop a new variable screening method for the varying coefficient Cox model based on the kernel smoothing and group learning methods. The sure screening property is established for ultra-high-dimensional settings. In addition, an iterative groupwise hard-thresholding algorithm is developed to implement our method. Simulation studies are conducted to evaluate the finite sample performances of the proposed method. An application to an ovarian cancer dataset is provided.
