# A novel characterization and new simple tests of multivariate independence using copulas

## TL;DR

This paper introduces a new characterization of multivariate independence using copulas and proposes simple, efficient tests based on the checkerboard approximation and various divergence metrics, validated through simulations and real data.

## Contribution

It provides a novel copula-based characterization of independence and develops new, computationally feasible tests with strong theoretical guarantees.

## Key findings

- Tests perform well in simulations across different dimensions.
- Methods are easy to implement and simulate under various hypotheses.
- Real data example demonstrates practical applicability.

## Abstract

The purpose of this paper is twofold. First, we provide a novel characterization of independence of random vectors based on the checkerboard approximation to a multivariate copula. Using this result, we then propose a new family of tests of multivariate independence for continuous random vectors. The tests rely on estimating the checkerboard approximation by means of the sample copula recently introduced in [9] and improved in [11]. Such estimators have nice properties, including a Glivenko-Cantelli-type theorem that guarantees almost-sure uniform convergence to the checkerboard approximation. Each of our test statistics is defined in terms of one of a number of different metrics, including the supremum, total variation and Hellinger distances, as well as the Kullback-Leibler divergence. All of these tests can be easily implemented since the corresponding test statistics can be efficiently simulated under any alternative hypothesis, even for moderate and large sample sizes in relatively large dimensions. Finally, we assess the performance of our tests by means of a simulation study and provide one real data example.