吉林大学学报(理学版) ›› 2025, Vol. 63 ›› Issue (5): 1325-1336.

• • 上一篇    下一篇

具有相依Bernoulli计数序列的RCINAR(p)模型的极大似然估计

李琪, 刘秀芳   

  1. 太原理工大学 数学学院, 太原 030024
  • 收稿日期:2024-12-19 出版日期:2025-09-26 发布日期:2025-09-26
  • 通讯作者: 刘秀芳 E-mail:liuxiufang@tyut.edu.cn

Maximum Likelihood Estimation of RCINAR (p) Model with Dependent Bernoulli Counting Series

LI Qi, LIU Xiufang   

  1. College of Mathematics, Taiyuan University of Technology, Taiyuan 030024, China
  • Received:2024-12-19 Online:2025-09-26 Published:2025-09-26

摘要: 用具有相依Bernoulli计数序列的p阶随机系数整数值自回归RCINAR(p)模型解决计数变量存在相关性特征数据的分析问题, 得到了该模型的统计性质、 参数条件极大似然估计及其渐近正态性, 并通过实际数据分析验证了模型的有效性, 精准捕捉了数据关联与趋势. 结果表明, 该模型的估计量随样本量递增收敛于真值.

关键词: 相依Bernoulli计数序列, 随机系数, 条件极大似然估计, 渐近正态性

Abstract: We used a p-order random coefficient integer autoregressive RCINAR(p) model with dependent Bernoulli counting series to solve the analysis problem  of data with correlated characteristics of counting variables. We obtained the statistical properties of the model, the conditional maximum likelihood estimation of the parameters, and their asymptotic normality, and the effectiveness of the model was verified through the analysis of actual data,  accurately capturing data  correlations and trends. These results show  that the estimators of the model converge to the true values as the sample size increases.

Key words: dependent Bernoulli counting series, random coefficient, conditional maximum likelihood estimation, asymptotic normality

中图分类号: 

  • O212.1