Coverage correlation: detecting singular dependencies between random variables

TL;DR

This paper introduces the coverage correlation coefficient, a new nonparametric measure for detecting singular dependencies between random variables, capable of identifying complex associations efficiently.

Contribution

It proposes the coverage correlation coefficient, a novel, distribution-free measure that estimates divergence and detects singular dependencies, extending to multivariate cases with efficient computation.

Findings

Consistently estimates an $f$-divergence between joint and marginal distributions.

Identifies independence and singular copula cases accurately.

Efficiently detects complex, nonlinear associations in large datasets.

Abstract

We introduce the coverage correlation coefficient, a novel nonparametric measure of statistical association designed to quantifies the extent to which two random variables have a joint distribution concentrated on a singular subset with respect to the product of the marginals. Our correlation statistic consistently estimates an $f$-divergence between the joint distribution and the product of the marginals, which is 0 if and only if the variables are independent and 1 if and only if the copula is singular. Using Monge--Kantorovich ranks, the coverage correlation naturally extends to measure association between random vectors. It is distribution-free, admits an analytically tractable asymptotic null distribution, and can be computed efficiently, making it well-suited for detecting complex, potentially nonlinear associations in large-scale pairwise testing.