Stein's functional method for weakly dependent random variables

Abstract

This article extends Stein's functional method to establish explicit rates of convergence in the Wasserstein-1 distance for Donsker's invariance principle under weak dependence. Our approach first reduces the problem of bounding distances between distributions on a function space to estimating distances between finite-dimensional marginals. We then employ the block technique, which is standard in Stein's methodology, to handle dependence. While our analysis focuses on $ϕ$-mixing sequences, the method extends to other forms of weak dependence admitting suitable covariance inequalities.