Stein's method compares probability distributions through the study of a class of linear
operators called Stein operators. While mainly studied in probability and used to underpin …

Abstract We propose two Euler–Maruyama (EM) type numerical schemes in order to
approximate the invariant measure of a stochastic differential equation (SDE) driven by an α …

We derive upper bounds on the Wasserstein distance (W 1), with respect to sup-norm,
between any continuous R d valued random field indexed by the n-sphere and the …

We present, in a unified way, a Stein methodology for infinitely divisible laws (without
Gaussian component) having finite first moment. Via a covariance representation, we obtain …

We generalize Catoni's M-estimator, put forward in [3] by Catoni under finite variance
assumption, to the case in which distributions can have finite α-th moment with α∈(1, 2). Our …

We obtain a Stein characterisation of the distribution of the product of two correlated normal
random variables with non-zero means, and more generally the distribution of the sum of …

This work explores and develops elements of Stein's method of approximation in the
infinitely divisible setting, and its connections to functional analysis. It is mainly concerned …

This paper is concerned with the Stein's method associated with a (possibly) asymmetric α α-
stable distribution Z, in dimension one. More precisely, its goal is twofold. In the first part, we …

First-order covariance inequalities via Stein's method Page 1 Bernoulli 26(3), 2020, 2051–2081
https://doi.org/10.3150/19-BEJ1182 First-order covariance inequalities via Stein’s method …

We present some recent developments for limit theorems in probability theory, illustrating the
variety of this field of activity. The recent results we discuss range from Stein's method, as …