Quantitative Strong Laws of Large Numbers

被引:1
|
作者
Neri, Morenikeji [1 ]
机构
[1] Univ Bath, Bath, England
来源
ELECTRONIC JOURNAL OF PROBABILITY | 2025年 / 30卷
基金
英国工程与自然科学研究理事会;
关键词
Laws of Large Numbers; large deviations; limit theorems; proof mining; CONVERGENCE-RATES; DEVIATIONS; PROOF; SUMS;
D O I
10.1214/25-EJP1280
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
We give novel computationally effective limit theorems for the convergence of the Cesaro-means of certain sequences of random variables. These results are intimately related to various Strong Laws of Large Numbers and, in that way, allow for the extraction of quantitative versions of many of these results. In particular, we produce optimal polynomial bounds in the case of pairwise independent random variables with uniformly bounded variance, improving on known results; furthermore, we obtain a new Baum-Katz type result for this class of random variables. Lastly, we are able to provide a fully quantitative version of a recent result of Chen and Sung that encompasses many limit theorems in the Strong Laws of Large Numbers literature.
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页数:22
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