We discuss briefly the very interesting concept of Brownian distance covariance developed
by Székely and Rizzo [Ann. Appl. Statist.(2009), to appear] and describe two possible …

We discuss briefly the very interesting concept of Brownian distance covariance developed
by Székely and Rizzo (2009) and describe two possible extensions. The first extension is for …

1. Introduction. A dependence statistic, the Brownian Distance Covariance, has been
proposed for use in dependence measurement and independence testing: we refer to this …

In this paper, we study distance covariance, Hilbert–Schmidt covariance (aka Hilbert–
Schmidt independence criterion [In Advances in Neural Information Processing Systems …

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 …

Distance covariance and distance correlation are scalar coefficients that characterize
independence of random vectors in arbitrary dimension. Properties, extensions 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 …

Distance correlation is a new measure of dependence between random vectors. Distance
covariance and distance correlation are analogous to product-moment covariance and …

Statistical inference on conditional dependence is essential in many fields including genetic
association studies and graphical models. The classic measures focus on linear conditional …

Distance covariance and distance correlation are non-negative real numbers that
characterize the independence of random vectors in arbitrary dimensions. In this work we …