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arXiv 1802.02244math.PR
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De Finetti定理:Kolmogorov距离中的收敛速率

De Finetti's theorem: rate of convergence in Kolmogorov distance

Emanuele Dolera, Stefano Favaro

摘要:

本文给出了de Finetti大数定律的一个定量形式。给定可交换伯努利变量的无限序列$\{X_n\}_{n \geq 1}$,众所周知,对于在$[0,1]$中取值的合适的随机变量$Y$,$\frac{1}{n} \sum_{i = 1}^n X_i \stackrel{a.s.}{\longrightarrow} Y$。在这里,我们考虑$\frac{1}{n} \sum_{i = 1}^n X_i$向$Y$的收敛速度收敛速率,相对于柯尔莫哥洛夫距离。在证明对于任何$α\in (0,1]$都可以获得任何$1/n^α$类型的速率之后,我们找到了$Y$概率分布的一个充分条件,以实现最佳收敛速率,即$1/n$。我们的主要结果改善现有的文献:特别是,对于\cite{MPS},我们研究了一个更强的度量,而对于\cite{Mna},我们削弱了$Y$的概率分布的规律性假设。

英文摘要:

This paper provides a quantitative version of de Finetti law of large numbers. Given an infinite sequence $\{X_n\}_{n \geq 1}$ of exchangeable Bernoulli variables, it is well-known that $\frac{1}{n} \sum_{i = 1}^n X_i \stackrel{a.s.}{\longrightarrow} Y$, for a suitable random variable $Y$ taking values in $[0,1]$. Here, we consider the rate of convergence in law of $\frac{1}{n} \sum_{i = 1}^n X_i$ towards $Y$, with respect to the Kolmogorov distance. After showing that any rate of the type of $1/n^α$ can be obtained for any $α\in (0,1]$, we find a sufficient condition on the probability distribution of $Y$ for the achievement of the optimal rate of convergence, that is $1/n$. Our main result improve on existing literature: in particular, with respect to \cite{MPS}, we study a stronger metric while, with respect to \cite{Mna}, we weaken the regularity hypothesis on the probability distribution of $Y$.

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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