Energy distance is a statistical distance between the distributions of random vectors, which
characterizes equality of distributions. The name energy derives from Newton's gravitational …

To classify data whether it is in the field of neural networks or maybe it is any application of
Biometrics viz: Handwriting classification or Iris detection, feasibly the most candid classifier …

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 …

Distance correlation is extended to the problem of testing the independence of random
vectors in high dimension. Distance correlation characterizes independence and determines …

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 …

We extend the theory of distance (Brownian) covariance from Euclidean spaces, where it
was introduced by Székely, Rizzo and Bakirov, to general metric spaces. We show that for …

Spatial transcriptomics (ST) technologies allow researchers to examine transcriptional
profiles along with maintained positional information. Such spatially resolved transcriptional …

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 …

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 …

Due to the variability of working conditions and the scarcity of fault samples, the existing
diagnosis models still have a big gap under the condition of covering more practical …