期望Wasserstein距离中经验测度的收敛性:Rd中的非渐近显式界

IF 0.6 4区数学 Q4 STATISTICS & PROBABILITY

Esaim-Probability and Statistics Pub Date : 2022-09-02 DOI:10.1051/ps/2023011

N. Fournier

引用次数: 6

摘要

摘要我们提供了一些具有显式常数的非渐近边界，用于测量与R d上给定概率分布的N个样本相关联的经验测度在期望Wasserstein距离中的收敛速度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Convergence of the empirical measure in expected Wasserstein distance: non asymptotic explicit bounds in Rd

Abstract. We provide some non asymptotic bounds, with explicit constants, that measure the rate of convergence, in expected Wasserstein distance, of the empirical measure associated to an i.i.d. N -sample of a given probability distribution on R d .

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来源期刊

Esaim-Probability and Statistics STATISTICS & PROBABILITY-

CiteScore

1.00

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： The journal publishes original research and survey papers in the area of Probability and Statistics. It covers theoretical and practical aspects, in any field of these domains. Of particular interest are methodological developments with application in other scientific areas, for example Biology and Genetics, Information Theory, Finance, Bioinformatics, Random structures and Random graphs, Econometrics, Physics. Long papers are very welcome. Indeed, we intend to develop the journal in the direction of applications and to open it to various fields where random mathematical modelling is important. In particular we will call (survey) papers in these areas, in order to make the random community aware of important problems of both theoretical and practical interest. We all know that many recent fascinating developments in Probability and Statistics are coming from "the outside" and we think that ESAIM: P&S should be a good entry point for such exchanges. Of course this does not mean that the journal will be only devoted to practical aspects.