ABSTRACT Extracting important features from ultra-high dimensional data is one of the primary tasks in statistical learning, information theory, precision medicine, and biological discovery. Many of the sure independent screening methods developed to meet these needs are suitable for special models under some assumptions. With the availability of more data types and possible models, a model-free generic screening procedure with fewer and less restrictive assumptions is desirable. In this article, we propose a generic nonparametric sure independence screening procedure, called BCor-SIS, on the basis of a recently developed universal dependence measure: Ball correlation. We show that the proposed procedure has strong screening consistency even when the dimensionality is an exponential order of the sample size without imposing sub-exponential moment assumptions on the data. We investigate the flexibility of this procedure by considering three commonly encountered challenging settings in biological discovery or precision medicine: iterative BCor-SIS, interaction pursuit, and survival outcomes. We use simulation studies and real data analyses to illustrate the versatility and practicability of our BCor-SIS method. Supplementary materials for this article are available online.

中文翻译：

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通用的确定独立性筛选程序

摘要 从超高维数据中提取重要特征是统计学习、信息论、精准医学和生物发现的主要任务之一。许多为满足这些需求而开发的可靠的独立筛选方法在某些假设下适用于特殊模型。随着更多数据类型和可能模型的出现，需要一种限制性假设越来越少的无模型通用筛选程序。在本文中，我们基于最近开发的通用依赖性度量：Ball 相关性，提出了一种通用的非参数确定独立性筛选程序，称为 BCor-SIS。我们表明，即使维度是样本大小的指数级，所提出的程序也具有很强的筛选一致性，而不对数据强加次指数矩假设。我们通过考虑生物发现或精准医学中三种常见的挑战性环境来研究该过程的灵活性：迭代 BCor-SIS、相互作用追求和生存结果。我们使用模拟研究和真实数据分析来说明我们的 BCor-SIS 方法的多功能性和实用性。本文的补充材料可在线获取。