吉林大学学报(理学版) ›› 2021, Vol. 59 ›› Issue (1): 49-54.

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非线性自回归模型误差密度估计的Berry-Esseen界

刘天泽1, 张勇2, 谭希丽1   

  1. 1. 北华大学 数学与统计学院, 吉林 吉林 132013; 2. 吉林大学 数学研究所, 长春 130012
  • 收稿日期:2020-03-27 出版日期:2021-01-26 发布日期:2021-01-26
  • 通讯作者: 张勇 E-mail:zyong2661@jlu.edu.cn

Berry-Esseen Bound for Error Density Estimators in Nonlinear Autoregressive Models

LIU Tianze1, ZHANG Yong2, TAN Xili1   

  1. 1. College of Mathematics and Statistics, Beihua University, Jilin 132013, Jilin Province, China; 2. Institute of Mathematics, Jilin University, Changchun 130012, China
  • Received:2020-03-27 Online:2021-01-26 Published:2021-01-26

摘要: 考虑非线性自回归模型Xi=rθ(Xi-1,…,Xi-s)+εi, 其中: θ为q维未知参数; {εi}为独立同分布的随机误差, 且均值为0、方差为σ2. 在适当的假设条件下, 给出非线性自回归模型误差密度估计的Berry-Esseen界.

关键词: 非线性自回归模型, 核密度估计, Berry-Esseen界

Abstract: We consider the nonlinear autoregressive model Xi=rθ(Xi-1,…,Xi-s)+εi,, where θ is a q-dimensional unknown parameter, {εi} is an independent and identically distributed random error with a mean of zero, and a variance of σ2. We give the Berry-Esseen bound for error density estimators in nonlinear autoregressive models under appropriate assumptions.

Key words: nonlinear autoregressive model, kernel density estimator, Berry-Esseen bound

中图分类号: 

  • O211.4