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文章基本信息

  • 标题:Maximum Empirical Likelihood: Empty Set Problem
  • 本地全文:下载
  • 作者:Grendar, Marian ; Judge, George G.
  • 期刊名称:Journal of Agribusiness
  • 印刷版ISSN:0738-8950
  • 出版年度:2009
  • 出版社:Journal of Agribusiness
  • 摘要:In the Empirical Estimating Equations (E^3) approach to estimation and inference estimating equations are replaced by their data-dependent empirical counterparts. It is odd but with E^3 there are models where the E^3-based estimator does not exist for some data set, and does exist for others. This depends on whether or not a set of data-supported probability mass functions that satisfy the empirical estimating equations is empty for the data set. In a finite sample context, this unnoted feature invalidates methods of estimation and inference, such as the Maximum Empirical Likelihood, that operate within E^3. The empty set problem of E^3 is illustrated by several examples and possible remedies are discussed.
  • 关键词:statistical theory;statistics;mathematical analysis;mathematical statistic
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