报告题目：Simple Measures of Uncertainty for Model Selection
报 告 人：Jiming Jiang（蒋继明） University of California, Davis；江西财经大学
报告时间：2020年7月9日 11：00-12：00
报告地点：Zoom 会议 ID：879 3820 0735
会议链接：https://us02web.zoom.us/j/87938200735
校内联系人：韩月才 hanyc@jlu.edu.cn
报告摘要：
We develop two simple measures of uncertainty for a model selection procedure. The first measure is similar in spirit to confidence set in parameter estimation; the second measure is focusing on error in model selection. The proposed methods are much simpler, both conceptually and computationally, than the existing measures of uncertain in model selection. We recognize major differences between model selection and traditional estimation or prediction problems, and propose reasonable frameworks, under which these measures are developed, and their asymptotic properties are established. Empirical studies demonstrate performance of the proposed measures, their superiority over the existing methods, and their relevance to real-life applications. Part of the work is jointly with Xiaohui Liu of Jiangxi University of Finance and Economics, and Yuanyuan Li of the University of California, Davis.
报告人简介：
Jiming Jiang, a professor of Statistics at the University of California, Davis. Professor Jiang’s research interests include mixed effects models, model selection, small area estimation, longitudinal data analysis, Big Data intelligence, statistical genetics/bioinformatics, pharmacokinetics, and asymptotic theory. He is author of five books and monographs, including Linear and Generalized Linear Mixed Models and Their Applications (Springer 2007), Large Sample Techniques for Statistics (Springer 2010), The Fence Methods (World Scientific 2016), Asymptotic Analysis of Mixed Effects Models: Theory, Application, and Open Problems (Chapman & Hall/CRC, 2017), and Robust Mixed Model Analysis (World Scientific 2019). He has served editorial boards (Associate Editor) of several major statistical journals including The Annals of Statistics and Journal of the American Statistical Association. Professor Jiang is a Fellow of the American Association for the Advancement of Science (AAAS; 美国科学促进协会), a Fellow of the American Statistical Association (ASA； 美国统计学会), a Fellow of the Institute of Mathematical Statistics (IMS；数理统计学会), and an Elected Member of the International Statistical Institute (ISI；国际统计研究院). He is a co-recipient of the Outstanding Statistical Application Award (ASA, 1998); the first corecipient of the NISS Alumni Achievement Award (National Institute of Statistical Sciences, USA, 2015).
