J4 ›› 2010, Vol. 48 ›› Issue (02): 219-225.

• 数学 • 上一篇    下一篇

INARS(p)模型的拟似然统计推断

薄海玲1, 张海祥2, 张哲2   

  1. 1. 长春工程学院 计算中心, 长春 130012|2. 吉林大学 数学学院, 长春 130012
  • 收稿日期:2009-06-26 出版日期:2010-03-26 发布日期:2010-03-22
  • 通讯作者: 张海祥 E-mail:zhx_math@163.com

QuasiLikelihood Statistical Inference for the INARS(p) Model

BO Hailing1, ZHANG Haixiang2, ZHANG Zhe2   

  1. 1. Computing Center, Changchun Institute of Technology, Changchun 130012, China;2. College of Mathematics, Jilin University, Changchun 130012, China
  • Received:2009-06-26 Online:2010-03-26 Published:2010-03-22
  • Contact: ZHANG Haixiang E-mail:zhx_math@163.com

摘要:

用拟似然方法对p阶基于符号伯努利稀疏算子的整值时间序列模型参数进行估计, 得出了参数修正的拟似然估计因子以及该估计因子的极限分布(可以用此极限分布对模型参数进行假设检验等统计分析), 并通过数值模拟, 将修正的拟似然估计与条件最小二乘估计进行了比较, 结果表明, 修正的拟似然估计在一定条件下明显优于条件最小二乘估计.

关键词: 整值模型; 符号伯努利稀疏算子; 条件最小二乘; 渐近分布

Abstract:

The authors  used quasilikelihood method to estimate the parameter of integervalued AR(p) process with signed binomial thinning to obtain the modifiedquasilikelihood estimator of the model parameters, and the asymptotic distribution of this estimator, thus, we can have some statistical analysis results about the model parameter, such as hypothesis testing. And  we compared the modifiedquasilikelihood estimator with the conditional least squares estimator via simulation. The results show that under some conditions,  the modifiedquasilikelihood estimator is better than conditional least squares estimator.

Key words: models of count data, signed Binomial thinning operator, conditional least squares, asymptotic distribution

中图分类号: 

  • O212.7