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arXiv math/0304373math.PR
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多维中心极限定理的强逼近估计

Estimates for the strong approximation in multidimensional central limit theorem

A. Yu. Zaitsev

摘要:

在最近的一篇论文中,作者得到了有限指数矩独立$\R^d$值随机向量和的强高斯逼近的最优界。这些结果可以被认为是Komlós-Major-Tusnády和Sakhanenko的著名结果的推广。明确给出了常数对维数$d$和被加数分布的依赖关系。并对有关问题进行了讨论。

英文摘要:

In a recent paper the author obtained optimal bounds for the strong Gaussian approximation of sums of independent $\R^d$-valued random vectors with finite exponential moments. The results may be considered as generalizations of well-known results of Komlós--Major--Tusnády and Sakhanenko. The dependence of constants on the dimension $d$ and on distributions of summands is given explicitly. Some related problems are discussed.

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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