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arXiv 2109.09274math.PR
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条件中心极限定理的Stein方法

Stein's method for Conditional Central Limit Theorem

Partha S. Dey, Grigory Terlov

摘要:

在七十年代,查尔斯·斯坦通过引入一种利用高斯分布的特征方程的方法,彻底改变了证明中心极限定理的方法。在过去的50年里,已经做了大量的研究,以适应和加强这种方法,以各种不同的设置和其他限制分布。然而,它还没有被扩展到研究条件收敛。在这篇文章中,我们开发了一种新的方法,使用Stein的方法可交换的对找到一个收敛速率的条件中心极限定理的形式$(X_n\mid Y_n=k)$,其中$(X_n, Y_n)$是渐近联合高斯,并将此结果扩展到一个多元版本。我们应用我们的一般结果几个具体的例子,包括随机二进制序列的模式计数和Erdös-Rényi随机图的子图计数。

英文摘要:

In the seventies, Charles Stein revolutionized the way of proving the Central Limit Theorem by introducing a method that utilizes a characterization equation for Gaussian distribution. In the last 50 years, much research has been done to adapt and strengthen this method to a variety of different settings and other limiting distributions. However, it has not been yet extended to study conditional convergences. In this article, we develop a novel approach using Stein's method for exchangeable pairs to find a rate of convergence in Conditional Central Limit Theorem of the form $(X_n\mid Y_n=k)$, where $(X_n, Y_n)$ are asymptotically jointly Gaussian, and extend this result to a multivariate version. We apply our general result to several concrete examples, including pattern count in a random binary sequence and subgraph count in Erdös-Rényi random graph.

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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