Abstract

We call a point process $Z$ on $\mathbb R$ exp-1-stable if for every $\alpha,\beta\in\mathbb R$ with $e^\alpha+e^\beta=1$, $Z$ is equal in law to $T_\alpha Z+T_\beta Z'$, where $Z'$ is an independent copy of $Z$ and $T_x$ is the translation by $x$. Such processes appear in the study of the extremal particles of branching Brownian motion and branching random walk and several authors have proven in that setting the existence of a point process $D$ on $\mathbb R$ such that $Z$ is equal in law to $\sum_{i=1}^\infty T_{\xi_i} D_i$, where $(\xi_i)_{i\ge1}$ are the atoms of a Poisson process of intensity $e^{-x}\,\mathrm d x$ on $\mathbb R$ and $(D_i)_{i\ge 1}$ are independent copies of $D$ and independent of $(\xi_i)_{i\ge1}$. In this note, we show how this decomposition follows from the classic LePage decomposition of a (union)-stable point process. Moreover, we give a short proof of it in the general case of random measures on $\mathbb R$.

Highlights

  • Let D be a point process on R, (Di)i≥1 be independent copies of D andi≥1 be the atoms of a Poisson process of intensity e−x dx on R and independent of (Di)i≥1

  • We show how this decomposition follows from the classic LePage decomposition of a-stable point process

  • It is easy to see that for every α, β ∈ R with eα +eβ = 1, Z is equal in law to TαZ +TβZ′, where Z′ is an independent copy of Z and Tx is the translation by x

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Summary

Introduction

Let D be a point process on R, (Di)i≥1 be independent copies of D and (ξi)i≥1 be the atoms of a Poisson process of intensity e−x dx on R and independent of (Di)i≥1. The purpose of this note is two-fold: First, we want to outline how the theory of stability in convex cones as developped by Davydov, Molchanov and Zuyev [12] yields the abovementioned LePage decomposition of stable point processes and with it the decomposition (1.1) This is the content of Section 2. Define BBM as follows: Starting with one initial particle at the point x of the real line, this particle performs Brownian motion until an exponentially distributed time of parameter 1/2, at which it splits into two particles. This is helpful for models where such a direct characterisation would be complicated, for example for branching random walks [18]

Stability in convex cones
Definitions and notation
Infinitely divisible random measures
Finiteness of the intensity

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