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

DOI10.15559/18-VMSTA116zbMath1417.62158arXiv1711.07778OpenAlexW2768630603WikidataQ129207087 ScholiaQ129207087MaRDI QIDQ2414854

Martin Keller-Ressel, Björn Böttcher, Rene L. Schilling

Publication date: 17 May 2019

Published in: Modern Stochastics. Theory and Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1711.07778

zbMATH Keywords

characteristic functionstochastic independenceGaussian random fielddistance covariancenegative definite functiondependence measure

Mathematics Subject Classification ID

Gaussian processes (60G15) Asymptotic properties of nonparametric inference (62G20) Measures of association (correlation, canonical correlation, etc.) (62H20) Characteristic functions; other transforms (60E10)

Related Items (6)

Generalization of the energy distance by Bernstein functions ⋮ Generalization of the HSIC and distance covariance using PDI kernels ⋮ Copula versions of distance multivariance and dHSIC via the distributional transform – a general approach to construct invariant dependence measures ⋮ Generalized martingale difference divergence: detecting conditional mean independence with applications in variable screening ⋮ Distance multivariance: new dependence measures for random vectors ⋮ A regression perspective on generalized distance covariance and the Hilbert-Schmidt independence criterion

Uses Software

• multivariance

Cites Work

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