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.

中文翻译：

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变系数Cox模型的非边际变量筛选方法

可变系数模型已成为描述协变量对响应的动态影响的非常流行的统计工具。在本文中，我们基于核平滑和组学习方法，为变系数Cox模型开发了一种新的变量筛选方法。确定的筛选属性适用于超高尺寸设置。此外，开发了一种迭代的基于组的硬阈值算法来实现我们的方法。仿真研究进行了评估该方法的有限样本性能。提供了对卵巢癌数据集的应用。