For ultrahigh-dimensional data, independent feature screening has been demonstrated both theoretically and empirically to be an effective dimension reduction method with low computational demanding. Motivated by the Buckley–James method to accommodate censoring, we propose a fused Kolmogorov–Smirnov filter to screen out the irrelevant dependent variables for ultrahigh-dimensional survival data. The proposed model-free screening method can work with many types of covariates (e.g. continuous, discrete and categorical variables) and is shown to enjoy the sure independent screening property under mild regularity conditions without requiring any moment conditions on covariates. In particular, the proposed procedure can still be powerful when covariates are strongly dependent on each other. We further develop an iterative algorithm to enhance the performance of our method while dealing with the practical situations where some covariates may be marginally unrelated but jointly related to the response. We conduct extensive simulations to evaluate the finite-sample performance of the proposed method, showing that it has favourable exhibition over the existing typical methods. As an illustration, we apply the proposed method to the diffuse large-B-cell lymphoma study.

对于超高维数据，独立特征筛选在理论上和经验上都被证明是一种有效的降维方法，计算要求低。受 Buckley-James 方法的启发以适应审查，我们提出了一个融合的 Kolmogorov-Smirnov 过滤器来筛选出与超高维生存数据无关的因变量。所提出的无模型筛选方法可以处理多种类型的协变量（例如连续变量、离散变量和分类变量），并且表明在温和的正则条件下具有确定的独立筛选特性，而不需要协变量的任何矩条件。特别是，当协变量相互强烈依赖时，所提出的过程仍然很强大。我们进一步开发了一种迭代算法来提高我们方法的性能，同时处理一些协变量可能与响应无关但共同相关的实际情况。我们进行了广泛的模拟来评估所提出方法的有限样本性能，表明它比现有的典型方法具有良好的表现。作为说明，我们将所提出的方法应用于弥漫性大 B 细胞淋巴瘤研究。