U.S.-France Cooperative Research: Model Selection and Loss Estimation

美法合作研究：模型选择和损失估计

• 批准号: 9726333
• 负责人: Martin Wells
• 金额: $ 1.5万
• 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: 1998
• 资助国家: 美国
• 起止时间: 1998-04-01 至 2001-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9726333&HistoricalAwards=false
• 关键词: U.S.; France; Cooperative; Research; Model

This three-year award provides support for US-France cooperative research in statistical modeling. The collaboration involves the research groups of Martin Wells at Cornell University and Dominique Fourdrinier at the University of Rouen in France. The proposed research will examine the model selection problem in statistical modeling. The investigators will use several methodologies, in particular, a mixture of frequentist, Bayesian, and asymptotic methods. The ultimate goal is to determine estimators that have desirable properties for a large class of distributions. The US investigator brings to this collaboration expertise in Bayesian statistics and decision theory. This is complemented by the French investigator's expertise in classical mathematics and analysis. The project will advance understanding of modern statistical theory and practice.

这个为期三年的奖项为美国和法国在统计建模方面的合作研究提供支持。这项合作包括康奈尔大学的Martin Wells和法国鲁昂大学的Dominique Fourdrinier研究小组。本研究将探讨统计建模中的模型选择问题。研究人员将使用几种方法，特别是频率、贝叶斯和渐近方法的混合。最终目标是确定对大量分布具有理想性质的估计器。这位美国研究者为这次合作带来了贝叶斯统计和决策理论方面的专业知识。这是由法国研究者在古典数学和分析方面的专业知识补充的。该项目将促进对现代统计理论和实践的理解。