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arXiv 2202.04717math.PR
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弱相依随机变量的矩量法中心极限定理

The Central Limit Theorem for Weakly Dependent Random Variables by the Moment Method

Michael Fleermann, Werner Kirsch

摘要:

本文对离散度量空间上的弱相关随机变量集合给出了一个中心极限定理,其中相关性随指标的距离而衰减.我们研究的相关结构完全依赖于混合矩的可分性。我们的研究给出了$α$ -混合随机变量以及非$α$ -混合随机变量(如MA($\infty$)过程)的CLT的新证明.特别地,我们的结果可以应用于独立白噪声的ARMA($p,q$)过程。

英文摘要:

In this paper, we derive a central limit theorem for collections of weakly correlated random variables indexed by discrete metric spaces, where the correlation decays in the distance of the indices. The correlation structure we study depends solely on the separability of mixed moments. Our investigation yields a new proof for the CLT for $α$-mixing random variables, but also non-$α$-mixing random variables fit within our framework, such as MA($\infty$) processes. In particular, our results can be applied to ARMA($p,q$) process with independent white noise.

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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