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平稳遍历函数型非参数递归M估计的收敛速度
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  • 英文篇名:Convergence rate of nonparametric recursive M-estimation for functional stationary ergodic data
  • 作者:陈楚曦 ; 凌能祥 ; 凌劲 ; 刘阳
  • 英文作者:CHEN Chuxi;LING Nengxiang;LING Jing;LIU Yang;School of Mathematics, Hefei University of Technology;Maintenance Branch, State Grid Anhui Electric Power Co., Ltd.;Environmental Monitoring Central Station of Anhui Province;
  • 关键词:M估计 ; 递归估计 ; 遍历数据 ; 几乎完全收敛 ; 收敛速度
  • 英文关键词:M-estimation;;recursive estimation;;ergodic data;;almost complete convergence;;convergence rate
  • 中文刊名:HEFE
  • 英文刊名:Journal of Hefei University of Technology(Natural Science)
  • 机构:合肥工业大学数学学院;国网安徽省电力有限公司检修公司;安徽省环境监测中心站;
  • 出版日期:2019-05-28
  • 出版单位:合肥工业大学学报(自然科学版)
  • 年:2019
  • 期:v.42;No.313
  • 语种:中文;
  • 页:HEFE201905022
  • 页数:5
  • CN:05
  • ISSN:34-1083/N
  • 分类号:140-144
摘要
文章基于平稳遍历函数型数据,构造非参数回归算子的递归M估计,在一定条件下建立了递归估计量的几乎完全一致收敛速度,推广了现有文献中的相关结果。
        In this paper, recursive method is adopted to study recursive M-estimation of functional nonparametric regression model for functional stationary ergodic data. Under some conditions, the uniform almost complete convergence rate of regression operator estimation is established. The results generalize the relevant results in the existing literature.
引文
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