Mathematical Sciences: Mathematical Statistics for Parametric and Nonparametric Procedures

数学科学：参数和非参数过程的数理统计

• 批准号: 9107842
• 负责人: Lawrence Brown
• 金额: --
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-07-15 至 1993-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9107842&HistoricalAwards=false
• 关键词: Mathematical; Sciences; Statistics; Parametric; Nonparametric

A classical focus of statistics is estimation of the accuracy or error of statistical procedures; currently, attention to this problem is directed at measuring accuracy after data have been obtained. By developing the underlying mathematical theory this research examines post-data inference and derives the properties of data dependent measures of accuracy. In addition, ancillarity paradoxes will be examined in which conditioning upon observed sample size within subgroup leads to inadmissibility of the usual estimators. The possibility of constructing and verifying dominating estimators will be investigated.

统计学的一个经典焦点是准确性的估计 或统计程序错误;目前，请注意这一点 问题是在数据已经被 得到了 通过发展基本的数学理论， 研究检查后数据推理，并得出的属性 数据相关的准确性测量。 此外，辅助 悖论将被审查，其中条件反射观察 子组内的样本量导致通常的 估计器。 构建和验证的可能性 将研究支配估计量。