Generalized Independence Test for Modern Data

Abstract

The test of independence is a crucial component of modern data analysis. However, traditional methods often struggle with the complex dependency structures found in high-dimensional data. To overcome this challenge, we introduce a novel test statistic that captures intricate relationships using similarity and dissimilarity information derived from the data. The statistic exhibits strong power across a broad range of alternatives for high-dimensional data, as demonstrated in extensive simulation studies. Under mild conditions, we show that the new test statistic converges to the $\chi^2_4$ distribution under the permutation null distribution, ensuring straightforward type I error control. Furthermore, our research advances the moment method in proving the joint asymptotic normality of multiple double-indexed permutation statistics. We showcase the practical utility of this new test with an application to the Genotype-Tissue Expression dataset, where it effectively measures associations between human tissues.