Multi-scale Fisher's independence test for multivariate dependence.

ABSTRACT: Identifying dependency in multivariate data is a common inference task that arises in numerous applications. However, existing nonparametric independence tests typically require computation that scales at least quadratically with the sample size, making it difficult to apply them in the presence of massive sample sizes. Moreover, resampling is usually necessary to evaluate the statistical significance of the resulting test statistics at finite sample sizes, further worsening the computational burden. We introduce a scalable, resampling-free approach to testing the independence between two random vectors by breaking down the task into simple univariate tests of independence on a collection of 2 × 2 contingency tables constructed through sequential coarse-to-fine discretization of the sample

SUBMITTER: Gorsky S 

PROVIDER: S-EPMC9648765 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature