Open Access
November 2019 Gaps and interleaving of point processes in sampling from a residual allocation model
Jim Pitman, Yuri Yakubovich
Bernoulli 25(4B): 3623-3651 (November 2019). DOI: 10.3150/19-BEJ1104


This article presents a limit theorem for the gaps $\widehat{G}_{i:n}:=X_{n-i+1:n}-X_{n-i:n}$ between order statistics $X_{1:n}\le\cdots\le X_{n:n}$ of a sample of size $n$ from a random discrete distribution on the positive integers $(P_{1},P_{2},\ldots)$ governed by a residual allocation model (also called a Bernoulli sieve) $P_{j}:=H_{j}\prod_{i=1}^{j-1}(1-H_{i})$ for a sequence of independent random hazard variables $H_{i}$ which are identically distributed according to some distribution of $H\in(0,1)$ such that $-\log(1-H)$ has a non-lattice distribution with finite mean $\mu_{\log}$. As $n\to\infty$ the finite dimensional distributions of the gaps $\widehat{G}_{i:n}$ converge to those of limiting gaps $G_{i}$ which are the numbers of points in a stationary renewal process with i.i.d. spacings $-\log(1-H_{j})$ between times $T_{i-1}$ and $T_{i}$ of births in a Yule process, that is $T_{i}:=\sum_{k=1}^{i}\varepsilon_{k}/k$ for a sequence of i.i.d. exponential variables $\varepsilon_{k}$ with mean 1. A consequence is that the mean of $\widehat{G}_{i:n}$ converges to the mean of $G_{i}$, which is $1/(i\mu_{\log})$. This limit theorem simplifies and extends a result of Gnedin, Iksanov and Roesler for the Bernoulli sieve.


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Jim Pitman. Yuri Yakubovich. "Gaps and interleaving of point processes in sampling from a residual allocation model." Bernoulli 25 (4B) 3623 - 3651, November 2019.


Received: 1 April 2018; Published: November 2019
First available in Project Euclid: 25 September 2019

zbMATH: 07110150
MathSciNet: MR4010967
Digital Object Identifier: 10.3150/19-BEJ1104

Keywords: GEM distribution , interleaving of simple point processes , residual allocation model , stars and bars duality , stationary renewal process , Yule process

Rights: Copyright © 2019 Bernoulli Society for Mathematical Statistics and Probability

Vol.25 • No. 4B • November 2019
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