分量相关的高维随机变量的最大点间距的渐进性质

Limit Law for the Maximum Interpoint Distance of High Dimensional Dependent Variables

  • 摘要: 本文中,我们考虑来自于p维总体\boldsymbolX_1,\boldsymbolX_2,\ldots,\boldsymbolX_n的次高斯随机变量之间的最大欧氏距离M_n=\max_1\leqslant i < j\leqslant n\left\|\boldsymbolX_i-\boldsymbolX_j\right\|。当总体的维数随着样本数趋近于无穷时,我们证明了M_n^2在合适的标准化条件下渐进服从于Gumbel分布。本文中的主要证明方法依赖于Stein-Chen Poisson近似和高维情形下的高斯近似。

     

    Abstract: In this paper, we consider the limiting distribution of the maximum interpoint Euclidean distance M_n=\max _1 \leqslant i < j \leqslant n\left\|\boldsymbolX_i-\boldsymbolX_j\right\|, where \boldsymbolX_1, \boldsymbolX_2, \ldots, \boldsymbolX_n be a random sample coming from a p dimensional population with dependent sub-gaussian components. When the dimension tends to infinity with the sample size, we proves that M_n^2 under a suitable normalization asymptotically obeys a Gumbel type distribution. The proofs mainly depend on the Stein-Chen Poisson approximation method and high dimensional Gaussian approximation.

     

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