吉林大学学报(理学版) ›› 2025, Vol. 63 ›› Issue (1): 24-0034.

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重复观测的Poisson-Lindley INAR(1)模型

刘瑞1, 朱复康1, 李琦2   

  1. 1. 吉林大学 数学学院, 长春 130012; 2. 长春师范大学 数学学院, 长春 130032
  • 收稿日期:2024-10-31 出版日期:2025-01-26 发布日期:2025-01-26
  • 通讯作者: 李琦 E-mail:joyli@vip.163.com

Replicatedly Observed Poisson-Lindley INAR(1) Model

LIU Rui1, ZHU Fukang1, LI Qi2   

  1. 1. College of Mathematics, Jilin University, Changchun 130012, China; 2. College of Mathematics, Changchun Normal University, Changchun 130032, China
  • Received:2024-10-31 Online:2025-01-26 Published:2025-01-26

摘要: 对过离散的重复观测时间序列数据, 考虑一种具有Poisson-Lindley边际分布的INAR(1)(PLINAR(1))过程的独立重复观测模型. 先通过条件最小二乘估计、 Yule-Walker估计、 拟似然估计和条件极大似然估计方法估计模型的参数, 讨论估计量的渐近性质, 并给出模型的预测, 再通过数值模拟比较不同估计方法的性能以及重复观测带来的影响, 最后对一组重复观测的每周太阳黑子群数量的数据进行拟合, 拟合结果验证了模型的有效性.

关键词: 重复观测, PLINAR(1)模型, 整数值时间序列, 过离散

Abstract: We considered an  independent replicatedly observed model of  INAR(1) (PLINAR(1)) process with Poisson-Lindley marginal distribution for overdispersed replicatedly observed time series data. Firstly, by using conditional least squares estimation, Yule-Walker estimation, quasi-likelihood estimation, and conditional maximum likelihood estimation methods to estimate the parameters of the model, we discussed the asymptotic properties of the estimators and gave predictions for the model. Secondly, through numerical simulations, the performance of different estimation methods and the impact of replicated observations were compared. Finally, a data set of the number of sunspot groups per week from replicated observations was fitted to this model, the fitting results validated the effectiveness of the model.

Key words: replicated observation, PLINAR(1) model, integer-valued time series, overdispersion

中图分类号: 

  • O212.1