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

这项为期三年的奖项为美法在统计建模方面的合作研究提供支持。康奈尔大学的马丁·威尔斯和法国鲁昂大学的多米尼克·福尔德尼耶的研究小组参与了这项合作。拟议的研究将考察统计建模中的模型选择问题。研究人员将使用几种方法，特别是频率法、贝叶斯法和渐近法的混合方法。最终目标是确定对一大类分布具有所需性质的估计量。这位美国调查人员将贝叶斯统计和决策理论方面的专业知识带到了这次合作中。此外，这位法国研究人员在古典数学和分析方面的专业知识也起到了补充作用。该项目将增进对现代统计理论和实践的理解。

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.