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题目： Interval Estimation Based on Raw Data
报告人：Weizhen Wang    (Wright State University, USA)

时间 ：2021年6月11日 上午10：30-11：30

地点 ：教二楼627教室 

摘要： Statistical inferences about parameters should depend on raw data only through minimal sufficient statistics – the well-known sufficiency principle. In this talk, we discuss an entry-level problem in Statistics: interval estimation for a proportion in a binomial or hypergeometric experiment. The proposed intervals, however, i) depend on the original random sample (the raw data) rather than a statistic that has a smaller sigma-algebra than the raw data, ii) are uniformly shorter than those intervals based on the total of the raw data, a minimal sufficient statistic in the two cases, and iii) are admissible. In practice, randomized confidence intervals are seldom used since such intervals may yield different conclusions on the same raw data. The proposed intervals violate the aforementioned principle if we only search for optimal intervals in the class of nonrandomized confidence intervals. If time permits, we will discuss an application in phase II clinical trial.
报告人简介: Weizhen Wang received his B.S. and M.S. at Peking University in 1987 and 1990, respectively, and completed his Ph.D. in Statistics at Cornell University in 1995. After one-year visit at Purdue University, he joined Wright State University, and has been a Professor of Statistics since 2007. His research includes bioequivalence, exact parametric and nonparametric inference, saturated and adaptive designs, categorical data analysis, foundation of statistics, statistical computation, dose-response study and causal inference. His current primary interest is exact statistical inference and its implementation in R.

主办单位：77779193永利官网 、交叉科学研究生院、 北京应用统计学会
联系人：胡涛