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arXiv 2402.05087math.PRmath.STstat.TH
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点过程强度测度的泛函极限定律及其应用

Functional limit laws for the intensity measure of point processes and applications

Giacomo Francisci, Anand N. Vidyashankar

AI总结:

本文为点过程强度测度建立泛函极限定理,推导半空间深度及分位数的渐近性质,并给出估计量的一致浓度界及分支随机游走中估计量的渐近正态性。

摘要:

受点过程生成的树索引随机变量的深度函数研究应用的启发,我们描述了点过程强度测度的泛函极限定理。具体而言,我们在一类有界可测函数上建立了强度测度估计的一致大数定律和一致中心极限定理。利用这些结果,我们推导了半空间深度的统一渐近性质,并作为推论,获得了标准化强度测度的中位数和其他分位数的渐近行为。此外,我们获得了半空间深度估计量的一致浓度上界。作为我们结果的一个推论,我们还推导了分支随机游走中点过程拉普拉斯变换的Lotka-Nagaev和Harris型估计量的一致相合性和一致渐近正态性。

英文摘要:

Motivated by applications to the study of depth functions for tree-indexed random variables generated by point processes, we describe functional limit theorems for the intensity measure of point processes. Specifically, we establish uniform laws of large numbers and uniform central limit theorems over a class of bounded measurable functions for estimates of the intensity measure. Using these results, we derive the uniform asymptotic properties of half-space depth and, as corollaries, obtain the asymptotic behavior of medians and other quantiles of the standardized intensity measure. Additionally, we obtain uniform concentration upper bound for the estimator of half-space depth. As a consequence of our results, we also derive uniform consistency and uniform asymptotic normality of Lotka-Nagaev and Harris-type estimators for the Laplace transform of the point processes in a branching random walk.

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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