Edgeworth Expansion by Stein's Method

TL;DR

This paper advances the use of Stein's method to derive two-term Edgeworth expansions for a broad class of distributions, overcoming previous limitations related to smoothness and continuity assumptions.

Contribution

It develops a new Stein's method approach for two-term Edgeworth expansions applicable to general cases, including non-smooth and discrete distributions.

Findings

Successfully derives two-term Edgeworth expansions using Stein's method

Overcomes limitations of previous methods requiring smoothness or continuity

Provides a general framework for distributional approximations

Abstract

Edgeworth expansion provides higher-order corrections to the normal approximation for a probability distribution. The classical proof of Edgeworth expansion is via characteristic functions. As a powerful method for distributional approximations, Stein's method has also been used to prove Edgeworth expansion results. However, these results assume that either the test function is smooth (which excludes indicator functions of the half line) or that the random variables are continuous (which excludes random variables having only a continuous component). Thus, how to recover the classical Edgeworth expansion result using Stein's method has remained an open problem. In this paper, we develop Stein's method for two-term Edgeworth expansions in a general case. Our approach involves repeated use of Stein equations, Stein identities via Stein kernels, and a replacement argument.