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This paper provides estimates for the convergence rate of the total variation distance in the framework of the Breuer-Major theorem, assuming some smoothness properties of the underlying function. The results are proved by applying new…

Probability · Mathematics 2018-07-26 David Nualart , Hongjuan Zhou

In this paper, we combine Hida distribution theory and Sobolev-Watanabe-Kree spaces in order to study finely the link between forward integrals obtained by regularization and Wick-It\^o integrals with respect to fractional Brownian motion…

Probability · Mathematics 2017-01-03 Benjamin Arras

We deal with stochastic differential equations with jumps. In order to obtain an accurate approximation scheme, it is usual to replace the "small jumps" by a Brownian motion. In this paper, we prove that for every fixed time $t$, the…

Probability · Mathematics 2022-12-15 Vlad Bally , Yifeng Qin

We deal with stochastic differential equations with jumps. In order to obtain an accurate approximation scheme, it is usual to replace the "small jumps" by a Brownian motion. In this paper, we prove that for every fixed time $t$, the…

Probability · Mathematics 2023-10-17 Vlad Bally , Yifeng Qin

We investigate the optimal rate of convergence in the multidimensional normal approximation of vector-valued Wiener-Ito integrals of which components all belong to the same fixed Wiener chaos. Combining Malliavin calculus, Stein's method…

Probability · Mathematics 2023-03-07 Huiping Chen

The paper studies upper bounds for the total variation distance between two polynomials of a special form in random vectors satisfying the Doeblin-type condition. Our approach is based on the recent results concerning Nikolskii--Besov-type…

Probability · Mathematics 2023-08-07 Egor Kosov , Anastasia Zhukova

We consider Riemann sum approximations of stochastic integrals with respect to the fractional Browian motion of index $H\geq \frac12$. We show the convergence of these schemes at first and second order. The processes obtained in the limit…

Probability · Mathematics 2021-12-20 Valentin Garino , Ivan Nourdin , Pierre Vallois

With the use of tensor product of Hilbert space, and a diagonalization procedure from operator theory, we derive an approximation formula for a general class of stochastic integrals. Further we establish a generalized Fourier expansion for…

Mathematical Physics · Physics 2015-05-13 Palle E. T. Jorgensen , Myung-Sin Song

This paper establishes an upper bound for the Kolmogorov distance between the maximum of a high-dimensional vector of smooth Wiener functionals and the maximum of a Gaussian random vector. As a special case, we show that the maximum of…

Statistics Theory · Mathematics 2019-02-07 Yuta Koike

We consider optimal approximation with respect to the mean square error of It\^o integrals and Skorohod integrals given an equidistant discretization of the Brownian motion. We obtain for suitable integrands optimal rates smaller than the…

Probability · Mathematics 2017-01-06 Peter Parczewski

We study the weighted total variation distance between probability measures. Using Fourier-analytic tools, we present estimates in terms of Wasserstein distances between the respective probabilities, under appropriate smoothness and moment…

Probability · Mathematics 2025-06-23 Iván Ivkovic , Miklós Rásonyi

We compute the Wassertein-1 (or Kolmogorov-Rubinstein) distance between a random walk in $R^d$ and the Brownian motion. The proof is based on a new estimate of the Lipschitz modulus of the solution of the Stein's equation. As an…

Probability · Mathematics 2025-04-29 L. Coutin , Laurent Decreusefond

Motivated by a theorem of Barbour, we revisit some of the classical limit theorems in probability from the viewpoint of the Stein method. We setup the framework to bound Wasserstein distances between some distributions on infinite…

Probability · Mathematics 2018-07-30 Laure Coutin , Laurent Decreusefond

Let ${F_n}$ be a sequence of random variables belonging to a finite sum of Wiener chaoses. Assume further that it converges in distribution towards $F_\infty$ satisfying ${\rm Var}(F_\infty)>0$. Our first result is a sequential version of a…

Probability · Mathematics 2012-10-08 Ivan Nourdin , Guillaume Poly

Using recent results on the behavior of multiple Wiener-It\^o integrals based on Stein's method, we prove Hsu-Robbins and Spitzer's theorems for sequences of correlated random variables related to the increments of the fractional Brownian…

Probability · Mathematics 2009-07-08 Ciprian Tudor

We consider a Markov chain on $\mathbb{R}^d$ with invariant measure $\mu$. We are interested in the rate of convergence of the empirical measures towards the invariant measure with respect to various dual distances, including in particular…

Probability · Mathematics 2022-10-13 Adrian Riekert

The goal of this paper is to estimate the total variation distance between two general stochastic polynomials. As a consequence one obtains an invariance principle for such polynomials. This generalizes known results concerning the total…

Probability · Mathematics 2019-12-03 Vlad Bally , Lucia Caramellino

We give bounds for the total variation distance between the solutions to two stochastic differential equations starting at the same point and with close coefficients, which applies in particular to the distance between an exact solution and…

Probability · Mathematics 2022-12-12 Pierre Bras , Gilles Pagès , Fabien Panloup

We provide a bound on a natural distance between finitely and infinitely supported elements of the unit sphere of $\ell^2(\mathbb{N}^*)$, the space of real valued sequences with finite $\ell^2$ norm. We use this bound to estimate the…

Probability · Mathematics 2019-08-20 Benjamin Arras , Ehsan Azmoodeh , Guillaume Poly , Yvik Swan

We study the convergence to the multiple Wiener-It\^{o} integral from processes with absolutely continuous paths. More precisely, consider a family of processes, with paths in the Cameron-Martin space, that converges weakly to a standard…

Probability · Mathematics 2007-12-27 Xavier Bardina , Maria Jolis , Ciprian Tudor
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