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Auteurs principaux: Abuqrais, Meshal, Pigoli, Davide
Format: Preprint
Publié: 2024
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Accès en ligne:https://arxiv.org/abs/2410.06164
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author Abuqrais, Meshal
Pigoli, Davide
author_facet Abuqrais, Meshal
Pigoli, Davide
contents The extension of bivariate measures of dependence to non-Euclidean spaces is a challenging problem. The non-linear nature of these spaces makes the generalisation of classical measures of linear dependence (such as the covariance) not trivial. In this paper, we propose a novel approach to measure stochastic dependence between two random variables taking values in a Riemannian manifold, with the aim of both generalising the classical concepts of covariance and correlation and building a connection to Fréchet moments of random variables on manifolds. We introduce generalised local measures of covariance and correlation and we show that the latter is a natural extension of Pearson correlation. We then propose suitable estimators for these quantities and we prove strong consistency results. Finally, we demonstrate their effectiveness through simulated examples and a real-world application.
format Preprint
id arxiv_https___arxiv_org_abs_2410_06164
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle A Riemannian covariance for manifold-valued data
Abuqrais, Meshal
Pigoli, Davide
Statistics Theory
The extension of bivariate measures of dependence to non-Euclidean spaces is a challenging problem. The non-linear nature of these spaces makes the generalisation of classical measures of linear dependence (such as the covariance) not trivial. In this paper, we propose a novel approach to measure stochastic dependence between two random variables taking values in a Riemannian manifold, with the aim of both generalising the classical concepts of covariance and correlation and building a connection to Fréchet moments of random variables on manifolds. We introduce generalised local measures of covariance and correlation and we show that the latter is a natural extension of Pearson correlation. We then propose suitable estimators for these quantities and we prove strong consistency results. Finally, we demonstrate their effectiveness through simulated examples and a real-world application.
title A Riemannian covariance for manifold-valued data
topic Statistics Theory
url https://arxiv.org/abs/2410.06164