Non-uniform Berry-Esseen Bound by Unbounded Exchangeable Pair Approach

09/30/2019
by   Dali Liu, et al.
0

Since Stein presented his ideas in the seminal paper s1, there have been a lot of research activities around Stein's method. Stein's method is a powerful tool to obtain the approximate error of normal and non-normal approximation. The readers are referred to Chatterjee c for recent developments of Stein's method. While several works on Stein's method pay attention to the uniform error bounds, Stein's method showed to be powerful on the non-uniform error bounds, too. By Stein's method, Chen and Shao cls1, cls2 obtained the non-uniform Berry-Esseen bound for independent and locally dependent random variables. The key in their works is the concentration inequality, which also has strong connection with another approach called the exchangeable pair approach. The exchangeable pair approach turned out to be an important topic in Stein's method. Let W be the random variable under study. The pair (W,W') is called an exchangeable pair if (W,W') and (W',W) share the same distribution. With Δ=W-W' , Rinott and Rotar rr, Shao and Su ss obtained the Berry-Esseen bound of the normal approximation when Δ is bounded. If Δ is unbounded, Chen and Shao cs2 provided a Berry-Esseen bound and got the optimal rate for an independence test. The concentration inequality plays a crucial role in previous studies, such as Shao and Su ss , Chen and Shao cs2. Recently, Shao and Zhang sz made a big break for unbounded Δ without using the concentration inequality. They obtained a simple bound as seen from the following result.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/29/2020

High-dimensional Central Limit Theorems by Stein's Method

We obtain explicit error bounds for the standard d-dimensional normal ap...
research
12/09/2018

Uniform Hanson-Wright type concentration inequalities for unbounded entries via the entropy method

This paper is devoted to uniform versions of the Hanson-Wright inequalit...
research
06/16/2018

Cramér-type Large deviation and non-uniform central limit theorems in high dimensions

Central limit theorems (CLTs) for high-dimensional random vectors with d...
research
02/27/2019

Improved Concentration Bounds for Conditional Value-at-Risk and Cumulative Prospect Theory using Wasserstein distance

Known finite-sample concentration bounds for the Wasserstein distance be...
research
03/15/2019

On Certifying Non-uniform Bound against Adversarial Attacks

This work studies the robustness certification problem of neural network...
research
01/05/2023

Another look at Stein's method for Studentized nonlinear statistics with an application to U-statistics

We take another look at using Stein's method to establish uniform Berry-...
research
08/30/2012

An Improved Bound for the Nystrom Method for Large Eigengap

We develop an improved bound for the approximation error of the Nyström ...

Please sign up or login with your details

Forgot password? Click here to reset