U.S.-France Cooperative Research: Construction and Evaluation of Accuracy Estimators

美法合作研究：精度估计器的构建和评估

• 批准号: 9216784
• 负责人: George Casella
• 金额: $ 1.7万
• 依托单位: Cornell Univ - State: AWDS MADE PRIOR MAY 2010
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-05-01 至 1996-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9216784&HistoricalAwards=false
• 关键词: U.S.; France; Cooperative; Research; Construction

This three-year award supports U.S.-France cooperative research in statistics and probability between George Casella of Cornell University and Christian Robert of the Laboratory for Theoretical and Applied Statistics, University of Paris VI. The research is concerned with statistical inferences from experiments. In addressing this problem they proposed to further develop procedures for accuracy estimation. Post-data statistical inferences are based on all information in an experiment, including the knowledge of the data. The proposed research will integrate and evaluate approaches to post-data inferences using decision theory and conditional inferences. They will investigate fundamental questions on the behavior of estimators under different loss functions and in loss function robustness and explore problems of assimilating decision theory with stochastic simulation techniques. The cooperative research project takes advantage of complementary interests and expertise. The U.S. investigator brings to this collaboration his expertise in decision theory. This is complemented by the French investigator's extensive experience in computational and algorithmic statistics. Together, their efforts may result in new techniques and methodology of statistical computing, including, better procedures for post data inferences and efficient methods for computation of accuracy measures.

这个为期三年的奖项支持康奈尔大学的乔治·卡塞拉和巴黎第六大学理论与应用统计实验室的克里斯蒂安·罗伯特在统计和概率方面的美法合作研究。 该研究涉及实验的统计推论。 为了解决这个问题，他们建议进一步开发准确度估计程序。 数据后统计推断基于实验中的所有信息，包括数据知识。 拟议的研究将使用决策理论和条件推理来整合和评估数据后推理的方法。 他们将研究估计器在不同损失函数下的行为和损失函数鲁棒性的基本问题，并探索将决策理论与随机模拟技术同化的问题。 合作研究项目利用了互补的兴趣和专业知识。这位美国调查员将他在决策理论方面的专业知识带入了这次合作。这位法国研究人员在计算和算法统计方面的丰富经验补充了这一点。他们的共同努力可能会产生统计计算的新技术和方法，包括更好的后数据推断程序和计算准确度测量的有效方法。