吉林大学学报(理学版) ›› 2023, Vol. 61 ›› Issue (5): 1083-1089.

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带有相依稀疏算子的一阶随机系数二项自回归模型

邰志艳, 王佳聪, 杨凯, 张洁   

  1. 长春工业大学 数学与统计学院, 长春 130012
  • 收稿日期:2022-12-14 出版日期:2023-09-26 发布日期:2023-09-26
  • 通讯作者: 张洁 E-mail:zhangjie@ccut.edu.cn

First-Order Random Coefficient Binomial Autoregressive Model with Dependent Thinning Operator

TAI Zhiyan, WANG Jiacong, YANG Kai, ZHANG Jie   

  1. School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, China
  • Received:2022-12-14 Online:2023-09-26 Published:2023-09-26

摘要: 将ADCBAR(1)模型推广到随机系数情形, 提出一种带有相依稀疏算子的一阶随机系数二项自回归模型RCADCBAR(1), 用于刻画具有相依性及零堆积性质的有限范围内整数值时间序列数据. 首先, 推导该模型的一些统计性质; 其次, 用条件最大似然方法对模型中存在的未知参数进行估计, 并讨论估计量的渐近性质; 最后, 将该模型应用到一组实际数据中, 以说明该模型的适用性.

关键词: 随机系数, 相依稀疏算子, RCADCBAR(1)模型, 条件最大似然估计

Abstract: The ADCBAR(1) model was extended to case of the random coefficient,  we proposed a class of first-order random coefficient binomial autoregressive model RCADCBAR(1) with dependent thinning operator, which could be used to characterize the finite-range integer-valued time series data with dependence and zero stacking properties. Firstly, some statistical properties of the model were derived. Secondly, the unknown parameters in the model were estimated by the conditional maximum likelihood method, and the asymptotic properties of estimators were discussed. Finally, the model was applied to a group of real data to illustrate the applicability of the model.

Key words: random coefficient, dependent thinning operator, RCADCBAR(1) model, conditional maximum likelihood estimation

中图分类号: 

  • O212.1