高维线性均值漂移模型的异常值检测
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  • 英文篇名:Outlier Detection for High-dimensional Linear Models With Mean Drifting
  • 作者:黄星寿 ; 赵培信
  • 英文作者:Huang Xingshou;Zhao Peixin;College of Mathematics and Statistics, Hechi University;College of Mathematics and Statistics, Chongqing Technology and Business University;
  • 关键词:高维线性模型 ; 异常值检测 ; 经验似然
  • 英文关键词:high-dimensional linear model;;outlier detection;;empirical likelihood
  • 中文刊名:TJJC
  • 英文刊名:Statistics & Decision
  • 机构:河池学院数学与统计学院;重庆工商大学数学与统计学院;
  • 出版日期:2019-07-23 13:14
  • 出版单位:统计与决策
  • 年:2019
  • 期:v.35;No.530
  • 基金:重庆市社会科学规划项目(2015PY24);; 重庆市基础与前沿研究计划项目(cstc2016jcyjA0151);; 重庆工商大学科研启动经费项目(2015-56-06);; 第五批重庆市高等学校优秀人才支持计划(2017CQ CU16)
  • 语种:中文;
  • 页:TJJC201914016
  • 页数:3
  • CN:14
  • ISSN:42-1009/C
  • 分类号:70-72
摘要
文章结合经验似然统计推断技术,对带有均值漂移的高维线性模型,给出了一个基于经验似然的模型异常值检测方法,该方法允许模型参数的维数随样本量的增加而趋于无穷,数据模拟表明所提出的异常值检测方法是行之有效的。
        Based on the empirical likelihood statistical inference technology, this paper presents a model outlier detection method based on empirical likelihood for high-dimensional linear models with mean drifting. This method allows the dimension of model parameters to tend to infinity with the increase of sample size. Data simulation shows that the outliers detection method is effective.
引文
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    [8]Wang T,Li Z H. Outlier Detection in High-Dimensional Regression Model[J]. Communications in Statistics-Theory and Methods, 2016,46(14).

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