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Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-22064

Barbour, A D; Månsson, M (2000). Compound Poisson approximation and the clustering of random points. Advances in Applied Probability, 32(1):19-38.

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Let n random points be uniformly and independently distributed in the unit square, and count the number W of subsets of k of the points which are covered by some translate of a small square C. If n|C| is small, the number of such clusters is approximately Poisson distributed, but the quality of the approximation is poor. In this paper, we show that the distribution of W can be much more closely approximated by an appropriate compound Poisson distribution CP(λ1, λ2,...). The argument is based on Stein's method, and is far from routine, largely because the approximating distribution does not satisfy the simplifying condition that iλi be decreasing.


4 citations in Web of Science®
5 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Mathematics
Dewey Decimal Classification:510 Mathematics
Uncontrolled Keywords:Compound Poisson approximation; Stein's method
Deposited On:04 Mar 2010 11:03
Last Modified:23 Nov 2012 14:18
Publisher:Applied Probability Trust
Publisher DOI:10.1239/aap/1013540020
Related URLs:http://www.jstor.org/stable/1428224

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