Herein, we propose a Spearman rank correlation based screening procedure for ultrahigh-dimensional data with censored response case. The proposed method is model-free without specifying any regression forms of predictors or response variable and is robust under the unknown monotone transformations of these response variable and predictors. The sure-screening and rank-consistency properties are established under some mild regularity conditions. Simulation studies demonstrate that the new screening method performs well in the presence of a heavy-tailed distribution, strongly dependent predictors or outliers and that offers superior performance over the existing nonparametric screening procedures. In particular, the new screening method still works well when a response variable is observed under a high censoring rate. An illustrative example is provided.
翻译:在此,我们建议采用基于Spearman级的、基于相关等级的超高维数据审查程序,并配有经过审查的答复个案。拟议方法不设任何回归式的预测器或反应变量的模型,也不具体说明任何回归式的预测器或反应变量,在这些响应变量和预测器的未知单质变形下具有强健性。在某种温和的常规条件下建立了可靠的筛选和排位一致性特性。模拟研究表明,新的筛选方法在出现大量尾量分布、依赖性强的预测器或外部线的情况下运作良好,而且比现有的非参数筛选程序具有优异性。特别是,当在高审查率下观察到一个响应变量时,新的筛选方法仍然运作良好。提供了一个示例。