Asymptotic Expansions for Additive Measures of Branching Brownian Motions

Pub Date : 2024-06-19 DOI:10.1007/s10959-024-01347-z
Haojie Hou, Yan-Xia Ren, Renming Song
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Abstract

Let N(t) be the collection of particles alive at time t in a branching Brownian motion in \(\mathbb {R}^d\), and for \(u\in N(t)\), let \({\textbf{X}}_u(t)\) be the position of particle u at time t. For \(\theta \in \mathbb {R}^d\), we define the additive measures of the branching Brownian motion by

$$\begin{aligned}{} & {} \mu _t^\theta (\textrm{d}{\textbf{x}}):= e^{-(1+\frac{\Vert \theta \Vert ^2}{2})t}\sum _{u\in N(t)} e^{-\theta \cdot {\textbf{X}}_u(t)} \delta _{\left( {\textbf{X}}_u(t)+\theta t\right) }(\textrm{d}{\textbf{x}}),\\{} & {} \quad \textrm{here}\,\, \Vert \theta \Vert \mathrm {is\, the\, Euclidean\, norm\, of}\,\, \theta . \end{aligned}$$

In this paper, under some conditions on the offspring distribution, we give asymptotic expansions of arbitrary order for \(\mu _t^\theta (({\textbf{a}}, {\textbf{b}}])\) and \(\mu _t^\theta ((-\infty , {\textbf{a}}])\) for \(\theta \in \mathbb {R}^d\) with \(\Vert \theta \Vert <\sqrt{2}\), where \((\textbf{a}, \textbf{b}]:=(a_1, b_1]\times \cdots \times (a_d, b_d]\) and \((-\infty , \textbf{a}]:=(-\infty , a_1]\times \cdots \times (-\infty , a_d]\) for \(\textbf{a}=(a_1,\cdots , a_d)\) and \(\textbf{b}=(b_1,\cdots , b_d)\). These expansions sharpen the asymptotic results of Asmussen and Kaplan (Stoch Process Appl 4(1):1–13, 1976) and Kang (J Korean Math Soc 36(1): 139–157, 1999) and are analogs of the expansions in Gao and Liu (Sci China Math 64(12):2759–2774, 2021) and Révész et al. (J Appl Probab 42(4):1081–1094, 2005) for branching Wiener processes (a particular class of branching random walks) corresponding to \(\theta ={\textbf{0}}\).

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分支布朗运动加法量的渐近展开
让N(t)是在\(\mathbb {R}^d\)中的分支布朗运动中在t时刻存活的粒子集合,对于\(u\in N(t)\),让\({\textbf{X}}}_u(t)\)是粒子u在t时刻的位置。对于 \(\theta \in \mathbb {R}^d\),我们用 $$\begin{aligned}{} & {} 来定义分支布朗运动的加法度量。\mu _t^\theta (\textrm{d}{\textbf{x}}):= e^{-(1+frac{Vert \theta \Vert ^2}{2})t}sum _{u\in N(t)} e^{-\theta \cdot {\textbf{X}}_u(t)} \delta _{left( {\textbf{X}}_u(t)+\theta t\right) }(\textrm{d}{\textbf{x}}),\{} & {}\textrm{here}\,\, \Vert \theta \Vert \mathrm {is\, the\, Euclidean\, norm\, of},\, \theta .\end{aligned}$$ 在本文中,在后代分布的一些条件下,我们给出了 \(\mu _t^\theta (({\textbf{a}}、{)和(((-\infty , {\textbf{a}}]))for \(\theta \in \mathbb {R}^d\) with \(\Vert \theta \Vert <;\其中 ((textbf{a}, textbf{b}]:=(a_1, b_1]times \cdots \times (a_d, b_d]\) and \((-\infty , \textbf{a}]:=(-\infty , a_1]\times \cdots \times (-\infty , a_d]\) for \(\textbf{a}=(a_1,\cdots , a_d)\) and \(\textbf{b}=(b_1,\cdots , b_d)\).这些展开使 Asmussen 和 Kaplan (Stoch Process Appl 4(1):1-13, 1976) 和 Kang (J Korean Math Soc 36(1):139-157, 1999)中的扩展结果,并且是 Gao 和 Liu (Sci China Math 64(12):2759-2774, 2021) 以及 Révész 等人(J Appl Probab 42(4):1081-1094, 2005)中针对分支维纳过程(一类特殊的分支随机游走)的扩展结果的类似结果,这些扩展结果对应于 \(\theta ={\textbf{0}}\).
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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