Hostname: page-component-8448b6f56d-cfpbc Total loading time: 0 Render date: 2024-04-23T21:34:05.146Z Has data issue: false hasContentIssue false

On the empty cells of Poisson histograms

Published online by Cambridge University Press:  14 July 2016

Wilfrid S. Kendall*
Affiliation:
University of Warwick
*
Postal address: Department of Statistics, University of Warwick, Coventry CV4 7AL, UK. E-mail: w.s.kendall@warwick.ac.uk

Abstract

This paper considers the histogram of unit cell size built up from m independent observations on a Poisson (μ) distribution. The following question is addressed: what is the limiting probability of the event that there are no unoccupied cells lying to the left of occupied cells of the histogram? It is shown that the probability of there being no such isolated empty cells (or isolated finite groups of empty cells) tends to unity as the number m of observations tends to infinity, but that the corresponding almost sure convergence fails. Moreover this probability does not tend to unity when the Poisson distribution is replaced by the negative binomial distribution arising when μ is randomized by a gamma distribution. The relevance to empirical Bayes statistical methods is discussed.

Type
Research Papers
Copyright
Copyright © Applied Probability Trust 1993 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Courant, R. and Hilbert, D. (1953) Methods of Mathematical Physics Volume I. Wiley, New York.Google Scholar
Holst, L. (1986) On birthdays, collectors, occupancy and other classical urn problems. Internat. Statist. Rev. 54, 1527.Google Scholar
Kinoshita, K. and Resnick, S. (1992) Convergence of scaled random samples in d. Ann. Prob. 19, 16401663.Google Scholar
Maritz, J. S. and Lwin, T. (1989) Empirical Bayes Methods, 2nd edn. Chapman and Hall, New York.Google Scholar
Robbins, H. E. (1955) An empirical Bayes approach to statistics. Proc. 3rd Berkeley Symp. Math. Statist. Prob. 1, 157163.Google Scholar