A general method, named the h-function method, is introduced to unify the constructions of level-α exact test and 1-α exact confidence interval. Using this method, any given confidence interval can be improved as follows: (i) an approximate interval, including a point estimator, is modified to an exact interval; (ii) an exact interval is refined to be an interval that is a subset of the previous one; (iii) two intervals, each utilizing partial data, are combined to form an exact interval that utilizes the complete data. Some real datasets, including Johnson & Johnson's Janssen vaccine (2021), are used to illustrate the method.
About the Speaker:
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 inferences, saturated and adaptive designs, categorical data analysis, foundation of statistics, statistical computation, and clinical trial. His current primary interest is exact statistical inference and the implementation in R.