Detecting independence of random vectors: generalized distance covariance and Gaussian covariance

Abstract

Distance covariance is a quantity to measure the dependence of two random vectors. We show that the original concept introduced and developed by Székely, Rizzo and Bakirov can be embedded into a more general framework based on symmetric Lévy measures and the corresponding real-valued continuous negative definite functions. The Lévy measures replace the weight functions used in the original definition of distance covariance. All essential properties of distance covariance are preserved in this new framework. From a practical point of view this allows less restrictive moment conditions on the underlying random variables and one can use other distance functions than Euclidean distance, e.g. Minkowski distance. Most importantly, it serves as the basic building block for distance multivariance, a quantity to measure and estimate dependence of multiple random vectors, which is introduced in a follow-up paper [Distance Multivariance: New dependence measures for random vectors (submitted). Revised version of arXiv: 1711.07775v1] to the present article.

arXiv commentPublished at https://doi.org/10.15559/18-VMSTA116 in the Modern Stochastics: Theory and Applications (https://www.i-journals.org/vtxpp/VMSTA) by VTeX (http://www.vtex.lt/)

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Cited by in corpus (5)

• Distance multivariance: New dependence measures for random vectors
• On complex Gaussian random fields, Gaussian quadratic forms and sample distance multivariance
• Copula versions of distance multivariance and dHSIC via the distributional transform -- a general approach to construct invariant dependence measures
• Dependence and dependence structures: estimation and visualization using the unifying concept of distance multivariance
• Notes on the interpretation of dependence measures