A Circular Chatterjee's Correlation Coefficient

TL;DR

This paper introduces a new circular version of Chatterjee's correlation coefficient that is intrinsic to circular data, consistent, and effective at detecting complex circular relationships.

Contribution

It proposes a novel circular Chatterjee's coefficient that removes arbitrary cut choices, retains key properties, and improves detection of multi-winding circular dependencies.

Findings

The new coefficient is zero under independence and one when the response is a measurable function of the predictor.

It is consistent and has a distribution-free null behavior under independence.

Simulations demonstrate its effectiveness for complex circular relationships like multi-winding cases.

Abstract

Chatterjee's rank correlation is a directed measure of association designed to detect whether one variable can be predicted as a function of another. While the original coefficient is naturally defined for real-valued data, circular data poses additional difficulty. Applying the usual construction requires cutting each circle at an arbitrary point and treating it as a line. Different choices of cut points can lead to different finite-sample values, even though the underlying circular relationship is unchanged. This paper proposes a circular version of Chatterjee's coefficient that removes this arbitrary choice. The population construction averages over response cuts in circular rank space, and the finite-sample construction averages over sample cut gaps and reduces to a simple statistic based only on cyclic ranks. The resulting coefficient is intrinsic to the circular ordering of the data, remains directed, and retains the key interpretation of Chatterjee's original coefficient. Under non-atomic circular marginals, it is zero exactly under independence and one exactly when the circular response is a measurable function of the circular predictor. We prove consistency and derive its distribution-free null behavior under independence. Simulations show that the proposed coefficient is especially useful for detecting multi-winding circular relationships, such as cases where the response goes around the circle twice or four times as the predictor goes around once, where standard circular correlations can be nearly blind.