Abstract Variable screening is a commonly used procedure in high dimensional data analysis to reduce dimensionality and ensure the applicability of available statistical methods. Such a procedure is complicated and computationally burdensome because spurious correlations commonly exist among predictor variables, while important predictor variables may not have large marginal correlations with the response variable. To circumvent these issues, in this paper, we develop a new screening technique, the “dynamic tilted current correlation screening” (DTCCS), for high dimensional variable screening. DTCCS is capable of selecting the most relevant predictors within a finite number of steps, and takes the popularly used sure independence screening (SIS) method and the high-dimensional ordinary least squares projection (HOLP) approach as its special cases. The DTCCS technique has sure screening and consistency properties which are justified theoretically and demonstrated numerically. A real example of gene expression data is analyzed using the proposed DTCCS procedure.

中文翻译：

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用于高维变量筛选的动态倾斜电流相关性

摘要 变量筛选是高维数据分析中常用的一种方法，用于降低维数并确保可用统计方法的适用性。由于预测变量之间通常存在虚假相关性，而重要的预测变量可能与响应变量没有大的边际相关性，因此这种过程很复杂且计算量很大。为了规避这些问题，在本文中，我们开发了一种新的筛选技术，即“动态倾斜电流相关筛选”（DTCCS），用于高维变量筛选。DTCCS 能够在有限的步数内选择最相关的预测变量，并将普遍使用的确定独立性筛选 (SIS) 方法和高维普通最小二乘投影 (HOLP) 方法作为其特例。DTCCS 技术具有可靠的筛选和一致性特性，这些特性在理论上得到了证明并在数值上得到了证明。使用建议的 DTCCS 程序分析基因表达数据的真实示例。