One-dimensional Stein's method with bespoke derivatives

TL;DR

This paper develops a new version of Stein's method using bespoke derivatives to effectively bound Wasserstein-1 distances between continuous and discrete distributions on the real line, with sharper bounds demonstrated across various models.

Contribution

It introduces bespoke weighted discrete derivatives and new bounds on Stein equation solutions for Integrated Pearson variables, advancing the precision of Stein's method.

Findings

Sharper bounds on Wasserstein-1 distance compared to existing literature

Effective application to diverse models including CLT and urn models

New family of weighted discrete derivatives for Stein's method

Abstract

We introduce a version of Stein's method of comparison of operators specifically tailored to the problem of bounding the Wasserstein-1 distance between continuous and discrete distributions on the real line. Our approach rests on a new family of weighted discrete derivative operators, which we call bespoke derivatives. We also propose new bounds on the derivatives of the solutions of Stein equations for Integrated Pearson random variables; this is a crucial step in Stein's method. We apply our result to several examples, including the Central Limit Theorem, Polya-Eggenberger urn models, the empirical distribution of the ground state of a many-interacting-worlds harmonic oscillator, the stationary distribution for the number of genes in the Moran model, and the stationary distribution of the Erlang-C system. Whenever our bounds can be compared with bounds from the literature, our constants are sharper.