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arXiv 2406.19166math.PR
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强大数定律的定量形式

Quantitative Strong Laws of Large Numbers

Morenikeji Neri

AI总结:

本文采用证明挖掘方法,为随机变量序列Cesaro均值的收敛性给出了计算有效的极限定理,提取了强大数定律的定量版本,改进了成对独立随机变量的多项式界,并获得了新的Baum-Katz型结果及Chen和Sung结果的完全定量版本。

摘要:

采用证明论中证明挖掘风格的方法,我们给出了某些随机变量序列的Cesaro均值的收敛性新的计算有效极限定理。这些结果与各种强大数定律密切相关,从而允许提取许多此类结果的定量版本。特别地,在一致有界方差的成对独立随机变量情形下,我们得出了最优多项式界,改进了已知结果;此外,针对此类随机变量,我们获得了一个新的Baum-Katz型结果。最后,我们能够提供Chen和Sung近期结果的完全定量版本,该结果涵盖了强大数定律文献中的许多极限定理。

英文摘要:

Using proof-theoretic methods in the style of proof mining, 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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