Multivariate normal approximation in geometric probability

MD Penrose, AR Wade

Research output: Contribution to journalArticle (Academic Journal)peer-review

7 Citations (Scopus)


Consider a measure $\mu_\lambda = \sum_x \xi_x \delta_x$ where the sum is over points $x$ of a Poisson point process of intensity $\lambda$ on a bounded region in $d$-space, and $\xi_x$ is a functional determined by the Poisson points near to $x$, i.e. satisfying an exponential stabilization condition, along with a moments condition (examples include statistics for proximity graphs, germ-grain models and random sequential deposition models). A known general result says the $\mu_\lambda$-measures (suitably scaled and centred) of disjoint sets in $R^d$ are asymptotically independent normals as $\lambda \to \infty$; here we give an $O(\lambda^{-1/(2d + \epsilon)})$ bound on the rate of convergence. We illustrate our result with an explicit multivariate central limit theorem for the nearest-neighbour graph on Poisson points on a finite collection of disjoint intervals.
Translated title of the contributionMultivariate normal approximation in geometric probability
Original languageEnglish
Pages (from-to)293 - 326
Number of pages34
JournalJournal of Statistical Theory and Practice
Volume2 (2)
Publication statusPublished - Jun 2008

Bibliographical note

Publisher: University of North Carolina Press

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