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Related papers: Optimal Berry-Esseen bounds on the Poisson space

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We propose a new functional analytic approach to Stein's method of exchangeable pairs that does not require the pair at hand to satisfy any approximate linear regression property. We make use of this theory in order to derive abstract…

Probability · Mathematics 2020-08-13 Christian Döbler

This article derives quantitative limit theorems for multivariate Poisson and Poisson process approximations. Employing the solution of Stein's equation for Poisson random variables, we obtain an explicit bound for the multivariate Poisson…

Probability · Mathematics 2021-06-01 Federico Pianoforte , Riccardo Turin

This article presents a new proof of the rate of convergence to the normal distribution of sums of independent, identically distributed random variables in chi-square distance, which was also recently studied in \cite{BobkovRenyi}. Our…

Probability · Mathematics 2017-11-15 Claire Delplancke , Laurent Miclo

An optimal bound on the quantiles of a certain kind of distributions is given. Such a bound is used in applications to Berry--Esseen-type bounds for nonlinear statistics.

Probability · Mathematics 2013-01-03 Iosif Pinelis

Let $\varUpsilon$ be the configuration space over a complete and separable metric base space, endowed with the Poisson measure $\pi$. We study the geometry of $\varUpsilon$ from the point of view of optimal transport and Ricci-lower bounds.…

Probability · Mathematics 2025-01-22 Lorenzo Dello Schiavo , Ronan Herry , Kohei Suzuki

We derive upper bounds on the Wasserstein distance ($W_1$), with respect to $\sup$-norm, between any continuous $\mathbb{R}^d$ valued random field indexed by the $n$-sphere and the Gaussian, based on Stein's method. We develop a novel…

Probability · Mathematics 2024-05-02 Krishnakumar Balasubramanian , Larry Goldstein , Nathan Ross , Adil Salim

The Berry-Ess\'{e}en upper bounds of moment estimators and least squares estimators of the mean and drift coefficients in Vasicek models driven by general Gaussian processes are studied. When studying the parameter estimation problem of…

Statistics Theory · Mathematics 2022-05-31 Yong Chen , Yumin Cheng

This paper is concerned by the study of barycenters for random probability measures in the Wasserstein space. Using a duality argument, we give a precise characterization of the population barycenter for various parametric classes of random…

Statistics Theory · Mathematics 2017-11-30 Jérémie Bigot , Thierry Klein

In this paper we use a Malliavin-Stein type method to investigate Poisson and normal approximations for the measurable functions of infinitely many independent random variables. We combine Stein's method with the difference operators in…

Probability · Mathematics 2018-08-13 Nguyen Tien Dung

In this work, we provide non-asymptotic bounds for the average speed of convergence of the empirical measure in the law of large numbers, in Wasserstein distance. We also consider occupation measures of ergodic Markov chains. One motivation…

Probability · Mathematics 2011-05-27 Emmanuel Boissard , Thibaut Le Gouic

This paper establishes a non-uniform Berry--Esseen bound for non-normal approximation using Stein's method. The main theorem generalizes the result of the authors in [Comptes Rendus Mathematique, 2024] to the context of non-normal…

Probability · Mathematics 2025-06-23 Lê Vǎn Thành , Nguyen Ngoc Tu

We provide a general bound on the Wasserstein distance between two arbitrary distributions of sequences of Bernoulli random variables. The bound is in terms of a mixing quantity for the Glauber dynamics of one of the sequences, and a simple…

Probability · Mathematics 2018-10-12 Gesine Reinert , Nathan Ross

We develop novel empirical Bernstein inequalities for the variance of bounded random variables. Our inequalities hold under constant conditional variance and mean, without further assumptions like independence or identical distribution of…

Statistics Theory · Mathematics 2026-05-28 Diego Martinez-Taboada , Aaditya Ramdas

We derive new Gaussian approximation for finite martingale difference sequences in $\mathbb{R}^d$ with respect to the Kolmogorov distance. Under appropriate conditions, our bounds exhibit a dependence of order $n^{-1/4}$ on the length of…

Probability · Mathematics 2026-05-07 Weichen Wu , Dung Le , Arun Kumar Kuchibhotla , Alessandro Rinaldo

We study the discrepancy between the distribution of a vector-valued functional of i.i.d. random elements and that of a Gaussian vector. Our main contribution is an explicit bound on the convex distance between the two distributions,…

Probability · Mathematics 2022-03-25 Mikołaj J. Kasprzak , Giovanni Peccati

Recent progress has been made in establishing normal approximation bounds in terms of the Wasserstein-$p$ distance for i.i.d. and locally dependent random variables. However, for $p > 1$, no such results have been demonstrated for dependent…

Probability · Mathematics 2025-02-25 Tianle Liu , Morgane Austern

In this paper, the uniformly asymptotic normality for sample quantiles of associated random variables is investigated under some conditions on the decay of the covariances. We obtain the rate of normal approximation of order…

Statistics Theory · Mathematics 2020-06-18 L. Douge

The Wasserstein distance quantifies the distance between two probability measures on a metric space. We prove an analogue of the Berry-Esseen inequality for the Wasserstein distance on a finite area hyperbolic surface. This inequality…

Number Theory · Mathematics 2025-12-18 Peter Humphries

We prove new concentration estimates for random variables that are functionals of a Poisson measure defined on a general measure space. Our results are specifically adapted to geometric applications, and are based on a pervasive use of a…

Probability · Mathematics 2015-04-14 Sascha Bachmann , Giovanni Peccati

We obtain Stein approximation bounds for stochastic integrals with respect to a Poisson random measure over ${\Bbb R}^d$, $d\geq 2$. This approach relies on third cumulant Edgeworth-type expansions based on derivation operators defined by…

Probability · Mathematics 2018-06-04 Nicolas Privault