Upper bounding the distance covariance of bounded random vectors

06/29/2023
by   John Çamkıran, et al.
0

A classical statistical inequality is used to show that the distance covariance of two bounded random vectors is bounded from above by a simple function of the dimensionality and the bounds of the random vectors. Two special cases that further simplify the result are considered: one in which both random vectors have the same number of components, each component taking values in an interval of unit length, and the other in which both random vectors have one component.

READ FULL TEXT

page 1

page 2

page 3

research
06/18/2021

Sharp Lower and Upper Bounds for the Covariance of Bounded Random Variables

In this paper we derive sharp lower and upper bounds for the covariance ...
research
03/17/2019

Stability of the Shannon-Stam inequality via the Föllmer process

We prove stability estimates for the Shannon-Stam inequality (also known...
research
02/11/2019

A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm

In this note, we derive concentration inequalities for random vectors wi...
research
11/21/2017

Detecting independence of random vectors I. Generalized distance covariance and Gaussian covariance

Distance covariance is a quantity to measure the dependence of two rando...
research
09/01/2022

A correlation inequality for random points in a hypercube with some implications

Let ≺ be the product order on ℝ^k and assume that X_1,X_2,…,X_n (n≥3) ar...
research
01/20/2020

On the Joint Typicality of Permutations of Sequences of Random Variables

Permutations of correlated sequences of random variables appear naturall...
research
06/25/2018

Distance covariance for discretized stochastic processes

Given an iid sequence of pairs of stochastic processes on the unit inter...

Please sign up or login with your details

Forgot password? Click here to reset