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

这个为期三年的奖项支持美国-法国合作研究 康奈尔大学的乔治卡塞拉和 大学和实验室的克里斯蒂安·罗伯特 巴黎第六大学理论与应用统计学。 的 研究关注的是统计推断， 实验 为了解决这个问题，他们建议进一步 制定准确度评估程序。 事后数据统计 推论是基于实验中的所有信息， 包括数据的知识。 拟议的研究将 整合和评估后数据推断方法， 决策理论和条件推理。 他们将调查 下估计量行为的基本问题 不同的损失函数和损失函数的鲁棒性， 探讨决策理论与随机理论的融合问题 仿真技术 该合作研究项目利用了 互补的兴趣和专业知识。美国调查员 为这次合作带来了他在决策理论方面的专业知识。这 与法国调查员广泛的 计算和算法统计经验。在一起， 他们的努力可能会导致新的技术和方法， 统计计算，包括更好的员额数据程序 精度计算的推论和有效方法 措施

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.