Measuring dependence between random vectors via optimal transport
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Publication:2078573
DOI10.1016/j.jmva.2021.104912zbMath1493.62359arXiv2104.14023OpenAlexW3158442583WikidataQ114157887 ScholiaQ114157887MaRDI QIDQ2078573
Publication date: 1 March 2022
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2104.14023
Asymptotic properties of parametric estimators (62F12) Measures of association (correlation, canonical correlation, etc.) (62H20)
Related Items (3)
Measuring association with Wasserstein distances ⋮ Scalable Model-Free Feature Screening via Sliced-Wasserstein Dependency ⋮ Measuring linear correlation between random vectors
Cites Work
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- Measuring and testing dependence by correlation of distances
- Limit laws of the empirical Wasserstein distance: Gaussian distributions
- Measuring association and dependence between random vectors
- Probit transformation for nonparametric kernel estimation of the copula density
- The distance between two random vectors wigh given dispersion matrices
- Applications and asymptotic power of marginal-free tests of stochastic vectorial independence
- The Fréchet derivative of an analytic function of a bounded operator with some applications
- The Frechet distance between multivariate normal distributions
- The Clarke and Michel-Penot subdifferentials of the eigenvalues of a symmetric matrix
- Efficient estimation in the bivariate normal copula model: Normal margins are least favourable
- A framework for measuring association of random vectors via collapsed random variables
- Convergence and concentration of empirical measures under Wasserstein distance in unbounded functional spaces
- Empirical optimal transport on countable metric spaces: distributional limits and statistical applications
- On the Bures-Wasserstein distance between positive definite matrices
- The Earth Mover's Correlation
- An Invitation to Statistics in Wasserstein Space
- Projection correlation between two random vectors
- Inequalities connecting the eigenvalues of a hermitian matrix with the eigenvalues of complementary principal submatrices
- RELATIONS BETWEEN TWO SETS OF VARIATES
- Inequalities: theory of majorization and its applications
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