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arXiv 1409.6380math.PR
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几何概率中Gibbsian和的正规逼近

Normal approximation of Gibbsian sums in geometric probability

Aihua Xia, J. E. Yukich

摘要:

本文研究了由窗口$Q_λ\uparrow R^d$上的Gibbsian空间点过程的泛函求和所得到的随机变量$W_λ$的渐近性态。我们建立了保证$W_λ$具有体积序涨落的条件,即它们与Poisson空间点过程泛函的涨落一致.我们将这个结果与Stein的方法相结合,推导出$W_λ$的正规逼近率,即$λ\to\infty$。我们的一般结果建立了随机几何统计和相关的欧氏图的Gibbsian输入的方差渐近和中心极限定理。我们还建立了类似的极限理论的索赔额的保险模型与Gibbsian输入,一个Gibbsian样本的最大点的数量,和空间的Gibbsian输入的出生增长模型的大小。

英文摘要:

This paper concerns the asymptotic behavior of a random variable $W_λ$ resulting from the summation of the functionals of a Gibbsian spatial point process over windows $Q_λ\uparrow R^d$. We establish conditions ensuring that $W_λ$ has volume order fluctuations, that is they coincide with the fluctuations of functionals of Poisson spatial point processes. We combine this result with Stein's method to deduce rates of normal approximation for $W_λ$, as $λ\to\infty$. Our general results establish variance asymptotics and central limit theorems for statistics of random geometric and related Euclidean graphs on Gibbsian input. We also establish similar limit theory for claim sizes of insurance models with Gibbsian input, the number of maximal points of a Gibbsian sample, and the size of spatial birth-growth models with Gibbsian input.

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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