ABSTRACT The additive Cox model is flexible and powerful for modelling the dynamic changes of regression coefficients in the survival analysis. This paper is concerned with feature screening for the additive Cox model with ultrahigh-dimensional covariates. The proposed screening procedure can effectively identify active predictors. That is, with probability tending to one, the selected variable set includes the actual active predictors. In order to carry out the proposed procedure, we propose an effective algorithm and establish the ascent property of the proposed algorithm. We further prove that the proposed procedure possesses the sure screening property. Furthermore, we examine the finite sample performance of the proposed procedure via Monte Carlo simulations, and illustrate the proposed procedure by a real data example.

中文翻译：

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超高维可加Cox模型中的特征筛选

摘要 加性Cox模型灵活而强大，可用于对生存分析中回归系数的动态变化进行建模。本文关注的是具有超高维协变量的加性 Cox 模型的特征筛选。所提出的筛选程序可以有效地识别主动预测因子。也就是说，当概率趋于 1 时，所选变量集包括实际的主动预测变量。为了执行所提出的过程，我们提出了一种有效的算法并建立了所提出算法的上升特性。我们进一步证明所提出的程序具有确定的筛选特性。此外，我们通过蒙特卡罗模拟检查了所提出的程序的有限样本性能，并通过真实的数据示例说明了所提出的程序。