数学物理学报 ›› 2023, Vol. 43 ›› Issue (5): 1440-1470.

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随机环境下两个上临界分支过程的参数比较

范协铨1,*(),胡海娟1,吴浩2,叶印娜3()   

  1. 1东北大学秦皇岛分校数学与统计学院 河北秦皇岛 066003
    2天津大学应用数学中心天津 300072
    3西交利物浦大学数学物理学院 江苏苏州 215123
  • 收稿日期:2022-06-30 修回日期:2023-03-24 出版日期:2023-10-26 发布日期:2023-08-09
  • 通讯作者: 范协铨 E-mail:fanxiequan@hotmail.com;yinna.ye@xjtlu.edu.cn
  • 作者简介:叶印娜,Email: yinna.ye@xjtlu.edu.cn
  • 基金资助:
    国家自然科学基金(11971063)

Comparison on the Criticality Parameters for Two Supercritical Branching Processes in Random Environments

Fan Xiequan1,*(),Hu Haijuan1,Wu Hao2,Ye Yinna3()   

  1. 1School of Mathematics and Statistics, Northeastern University at Qinhuangdao, Hebei Qinhuangdao 066003
    2Center for Applied Mathematics, Tianjin University, Tianjin 300072
    3School of Mathematics and Physics, Xi'an Jiaotong-Liverpool University, Jiangsu Suzhou 215123
  • Received:2022-06-30 Revised:2023-03-24 Online:2023-10-26 Published:2023-08-09
  • Contact: Xiequan Fan E-mail:fanxiequan@hotmail.com;yinna.ye@xjtlu.edu.cn
  • Supported by:
    NSFC(11971063)

摘要:

(Z1,n)n0(Z2,n)n0 是两个在独立同分布随机环境下的上临界分支过程, 并且其关键参数分别为 μ1μ2. 容易知道, 在适当条件下, 1nlnZ1,n1mlnZ2,m分别依概率收敛到 μ1μ2. 该文旨在讨论两个上临界分支过程的关键参数之差 μ1μ2 的估计问题, 它可以被看作是一类双样本 U 统计量问题. 我们得到了 1nlnZ1,n1mlnZ2,m 的中心极限定理, 非一致性 Berry-Esseen 估计和 Cramér 型中偏差. 最后, 作为应用部分, 指出了以上的结果可用于关键参数置信区间的构造.

关键词: 分支过程, 随机环境, Berry-Esseen 估计, Cramér 型中偏差

Abstract:

Let {Z1,n,n0} and {Z2,n,n0} be two supercritical branching processes in different random environments, with criticality parameters μ1 and μ2 respectively. It is known that with certain conditions, 1nlnZ1,nμ1 and 1mlnZ2,mμ2 in probability as m,n. In this paper, we are interested in the comparison on the two criticality parameters, which can be regarded as two-sample U-statistic. To this end, we prove a non-uniform Berry-Esseen's bound and Cramér's moderate deviations for 1nlnZ1,n1mlnZ2,m as m,n. An application is also given for constructing confidence intervals of μ1μ2.

Key words: Branching processes, Random environments, Berry-Esseen's bound, Cramér's moderate deviations

中图分类号: 

  • O211.65