Approximation rate in Wasserstein distance of probability measures on the real line by deterministic empirical measures - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Article Dans Une Revue Journal of Approximation Theory Année : 2022

Approximation rate in Wasserstein distance of probability measures on the real line by deterministic empirical measures

Résumé

We are interested in the approximation in Wasserstein distance with index $\rho\ge 1$ of a probability measure $\mu$ on the real line with finite moment of order $\rho$ by the empirical measure of $N$ deterministic points. The minimal error converges to $0$ as $N\to+\infty$ and we try to characterize the order associated with this convergence. Apart when $\mu$ is a Dirac mass and the error vanishes, the order is not larger than $1$. We give a necessary condition and a sufficient condition for the order to be equal to this threshold $1$ in terms of the density of the absolutely continuous with respect to the Lebesgue measure part of $\mu$. We also check that for the order to lie in the interval $\left(1/\rho,1\right)$, the support of $\mu$ has to be a bounded interval, and that, when $\mu$ is compactly supported, the order is not smaller than $1/\rho$. Last, we give a necessary and sufficient condition in terms of the tails of $\mu$ for the order to be equal to some given value in the interval $\left(0,1/\rho\right)$.
Fichier principal
Vignette du fichier
S0021904521001441.pdf (632.9 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03081116 , version 1 (08-01-2024)

Licence

Paternité - Pas d'utilisation commerciale

Identifiants

Citer

Oumaima Bencheikh, Benjamin Jourdain. Approximation rate in Wasserstein distance of probability measures on the real line by deterministic empirical measures. Journal of Approximation Theory, 2022, 274 (105684), ⟨10.1016/j.jat.2021.105684⟩. ⟨hal-03081116⟩
67 Consultations
7 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More