Annals of Applied Probability

The asymptotic distribution and Berry–Esseen bound of a new test for independence in high dimension with an application to stochastic optimization

Wei-Dong Liu, Zhengyan Lin, and Qi-Man Shao

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Let X1, …, Xn be a random sample from a p-dimensional population distribution. Assume that c1nαpc2nα for some positive constants c1, c2 and α. In this paper we introduce a new statistic for testing independence of the p-variates of the population and prove that the limiting distribution is the extreme distribution of type I with a rate of convergence $O((\log n)^{5/2}/\sqrt{n})$. This is much faster than O(1/log n), a typical convergence rate for this type of extreme distribution. A simulation study and application to stochastic optimization are discussed.

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Ann. Appl. Probab., Volume 18, Number 6 (2008), 2337-2366.

First available in Project Euclid: 26 November 2008

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Zentralblatt MATH identifier

Primary: 60F05: Central limit and other weak theorems
Secondary: 62F05: Asymptotic properties of tests

Independence test extreme distribution Berry–Esseen bound correlation matrices stochastic optimization


Liu, Wei-Dong; Lin, Zhengyan; Shao, Qi-Man. The asymptotic distribution and Berry–Esseen bound of a new test for independence in high dimension with an application to stochastic optimization. Ann. Appl. Probab. 18 (2008), no. 6, 2337--2366. doi:10.1214/08-AAP527.

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