An optimal Berry-Esseen type theorem for integrals of smooth functions
Lutz Mattner, Irina Shevtsova

TL;DR
This paper establishes a refined Berry-Esseen type inequality for smooth functions of convolutions of probability laws, providing sharper bounds and explicit equality conditions, especially in the i.i.d. case.
Contribution
It introduces an optimal Berry-Esseen inequality for smooth functions, improving classical bounds and characterizing equality cases for convolutions of probability measures.
Findings
Error bound is smaller than one-sixth of the Lyapunov ratio times the Lipschitz constant.
In the i.i.d. case, the approximating law is a standardized symmetric binomial distribution.
The inequality improves upon classical Berry-Esseen bounds for certain configurations.
Abstract
We prove a Berry-Esseen type inequality for approximating expectations of sufficiently smooth functions , like , with respect to standardized convolutions of laws on the real line by corresponding expectations based on symmetric two-point laws isoscedastic to the . Equality is attained for every possible constellation of the Lipschitz constant and the variances and the third centred absolute moments of the . The error bound is strictly smaller than times the Lyapunov ratio times , and tends to zero also if is fixed and the third standardized absolute moments of the tend to one. In the homoscedastic case of equal variances of the , and hence in particular in the i.i.d. case, the approximating law is a standardized symmetric binomial one. The…
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