Is it possible to define a coefficient of correlation which is (a) as simple as the classical
coefficients like Pearson's correlation or Spearman's correlation, and yet (b) consistently …

Correlation coefficients (CC) are statistical tools that measure how strong a relationship is
between two variables. In geochemical studies, these variables could be different elements' …

Multi-modal classification (MMC) aims to integrate the complementary information from
different modalities to improve classification performance. Existing MMC methods can be …

We propose a test of independence of two multivariate random vectors, given a sample from
the underlying population. Our approach is based on the estimation of mutual information …

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 …

Given a high-dimensional data set, we often wish to find the strongest relationships within it.
A common strategy is to evaluate a measure of dependence on every variable pair and …

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 …

We introduce an L 2-type test for testing mutual independence and banded dependence
structure for high dimensional data. The test is constructed on the basis of the pairwise …

Understanding and developing a correlation measure that can detect general dependencies
is not only imperative to statistics and machine learning, but also crucial to general scientific …

Detecting dependence between two random variables is a fundamental problem. Although
the Pearson correlation coefficient is effective for capturing linear dependence, it can be …