Energy distance is a metric that measures the distance between the distributions of random
vectors. Energy distance is zero if and only if the distributions are identical, thus it …

The energy of data is the value of a real function of distances between data in metric spaces.
The name energy derives from Newton's gravitational potential energy, which is also a …

In this study, we developed a modified version of the CRiteria Importance Through Inter-
criteria Correlation (CRITIC) method, namely the Distance Correlation-based CRITIC (D …

In this article, we propose a general framework for distribution-free nonparametric testing in
multi-dimensions, based on a notion of multivariate ranks defined using the theory of …

For distributed machine learning with sensitive data, we demonstrate how minimizing
distance correlation between raw data and intermediary representations reduces leakage of …

This article investigates the problem of testing independence of two random vectors of
general dimensions. For this, we give for the first time a distribution-free consistent test. Our …

Distance correlation has gained much recent attention in the data science community: the
sample statistic is straightforward to compute and asymptotically equals zero if and only if …

Classical dependence measures such as Pearson correlation, Spearman's ρ, and Kendall's
τ can detect only monotonic or linear dependence. To overcome these limitations, Székely et …

This article presents dcor, an open-source Python package dedicated to distance correlation
and other statistics related to energy distance. These energy statistics include distances …

Supplementary material to “Interaction pursuit in high-dimensional multi-response
regression via distance correlation”. Due to space constraints, the details about the post …