arXiv Open Access 2023

Central Limit Theorems and Approximation Theory: Part I

Arisina Banerjee Arun K Kuchibhotla

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Central limit theorems (CLTs) have a long history in probability and statistics. They play a fundamental role in constructing valid statistical inference procedures. Over the last century, various techniques have been developed in probability and statistics to prove CLTs under a variety of assumptions on random variables. Quantitative versions of CLTs (e.g., Berry--Esseen bounds) have also been parallelly developed. In this article, we propose to use approximation theory from functional analysis to derive explicit bounds on the difference between expectations of functions.

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math.ST stat.AP

Penulis (2)

Arisina Banerjee

Arun K Kuchibhotla

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Banerjee, A., Kuchibhotla, A.K. (2023). Central Limit Theorems and Approximation Theory: Part I. https://arxiv.org/abs/2306.05947

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Tahun Terbit: 2023
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓