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  • 标题:A Geometric Approach to Conditioning and the Search for Minimum Variance Unbiased Estimators
  • 本地全文:下载
  • 作者:James E. Marengo ; David L. Farnsworth
  • 期刊名称:Open Journal of Statistics
  • 印刷版ISSN:2161-718X
  • 电子版ISSN:2161-7198
  • 出版年度:2021
  • 卷号:11
  • 期号:3
  • 页码:437-442
  • DOI:10.4236/ojs.2021.113027
  • 语种:English
  • 出版社:Scientific Research Publishing
  • 摘要:Our purpose is twofold: to present a prototypical example of the conditioning technique to obtain the best estimator of a parameter and to show that this technique resides in the structure of an inner product space. The technique uses conditioning of an unbiased estimator on a sufficient statistic. This procedure is founded upon the conditional variance formula, which leads to an inner product space and a geometric interpretation. The example clearly illustrates the dependence on the sampling methodology. These advantages show the power and centrality of this process.
  • 关键词:Conditional Variance Formula;Conditioning;Geometric Representation;Minimum Variance Estimator;Rao-Blackwell Theorem;Sufficient Statistic;Unbiased Estimator
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