High-Dimensional Independence Testing via Maximum and Average Distance Correlations

• URL: http://arxiv.org/abs/2001.01095v2
• Date: Mon, 5 Feb 2024 20:24:50 GMT
• Title: High-Dimensional Independence Testing via Maximum and Average Distance Correlations
• Authors: Cencheng Shen, Yuexiao Dong
• Abstract summary: We characterize consistency properties in high-dimensional settings with respect to the number of marginally dependent dimensions. We examine the advantages of each test statistic, examine their respective null distributions, and present a fast chi-square-based testing procedure.
• Score: 5.756296617325109
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces and investigates the utilization of maximum and average distance correlations for multivariate independence testing. We characterize their consistency properties in high-dimensional settings with respect to the number of marginally dependent dimensions, assess the advantages of each test statistic, examine their respective null distributions, and present a fast chi-square-based testing procedure. The resulting tests are non-parametric and applicable to both Euclidean distance and the Gaussian kernel as the underlying metric. To better understand the practical use cases of the proposed tests, we evaluate the empirical performance of the maximum distance correlation, average distance correlation, and the original distance correlation across various multivariate dependence scenarios, as well as conduct a real data experiment to test the presence of various cancer types and peptide levels in human plasma.

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