Stein's method for the Beta distribution and the P\'olya-Eggenberger Urn

TL;DR

This paper applies Stein's method to derive optimal bounds on the Wasserstein distance between the scaled number of white balls in a Pólya-Eggenberger urn and its Beta distribution limit, also refining results for the Arcsine law.

Contribution

It introduces a novel application of Stein's method using characterizing equations for the Beta distribution and extends Stein's density approach to discrete distributions.

Findings

Derived optimal Wasserstein bounds for the urn model

Refined bounds for the Arcsine approximation with explicit constants

Demonstrated the bounds' rate is of the optimal order

Abstract

Using a characterizing equation for the Beta distribution, Stein's method is applied to obtain bounds of the optimal order for the Wasserstein distance between the distribution of the scaled number of white balls drawn from a P\'olya-Eggenberger urn and its limiting Beta distribution. The bound is computed by making a direct comparison between characterizing operators of the target and the Beta distribution, the former derived by extending Stein's density approach to discrete distributions. In addition, refinements are given to D\"obler's result [12] for the Arcsine approximation for the fraction of time a simple random walk of even length spends positive, and so also to the distributions of its last return time to zero and its first visit to its terminal point, by supplying explicit constants to the present Wasserstein bound and also demonstrating that its rate is of the optimal order.