Interpretation and visualization of distance covariance through additive decomposition of correlations formula
Methodology
2026-03-20 v1
Abstract
Distance covariance is a widely used statistical methodology for testing the dependency between two groups of variables. Despite the appealing properties of consistency and superior testing power, the testing results of distance covariance are often hard to be interpreted. This paper presents an elementary interpretation of the mechanism of distance covariance through an additive decomposition of correlations formula. Based on this formula, a visualization method is developed to provide practitioners with a more intuitive explanation of the distance covariance score.
Keywords
Cite
@article{arxiv.2305.14767,
title = {Interpretation and visualization of distance covariance through additive decomposition of correlations formula},
author = {Andi Wang and Hao Yan and Juan Du},
journal= {arXiv preprint arXiv:2305.14767},
year = {2026}
}
Comments
INFORMS Journal on Data Science 0(0) (2026)