时 间：2024年6月20日 15:00 - 16:00
地 点：普陀校区理科大楼A1114
报告人：蒋继明 加州大学戴维斯分校教授
主持人：於州 华东师范大学教授
摘 要：
We propose a new classified mixed model prediction (CMMP) procedure, called pseudoBayesian CMMP (PBCMMP), that utilizes “prior” information in matching the group index between the training data and new data, whose characteristics of interest one wishes to predict. The previous CMMP method (Jiang et al. 2018) does not incorporate such information; as a result, the CMMP method is not consistent in terms of matching the group index. Although, as the number of training data groups increases, the CMMP method can predict the mixed eNects of interest consistently, its accuracy is not guaranteed when the number of groups is moderate, as is the case in many potential applications. The PBCMMP procedure assumes a flexible working probability model for the group index of the new observation to match the index of a training data group, which may be viewed as a pseudo prior. We show that, given any working model satisfying mild conditions, the PBCMMP procedure is consistent and asymptotically optimal both in term of matching the group index and in terms of predicting the mixed eNect of interest associated with the new observations. The theoretical results are fully supported by results of empirical studies, including Monte-Carlo simulations and real-data validation. This work is joint with Haiqiang Ma of Jiangxi University of Finance and Economics
报告人简介：
蒋继明，现为加州大学戴维斯分校的统计学教授, 统计系系主任。1995年从加州大学伯克利分校取得博士学位。他的研究兴趣包括混合效应模型、模型选择、小区域估计、纵向数据分析、大数据智能、统计遗传学/生物信息学、药代动力学和渐近理论。他先后出版了五本专著，包括线性和广义线性混合模型及其应用（Springer 2007），统计学大样本理论（Springer 2010），栅栏方法（World Scientific 2016），混合效应模型的渐进分析：理论、应用和开放性问题（Chapman＆Hall / CRC，2017）和稳健混合模型分析（World Scientific 2019））。他曾担任过包括AoS、JASA等统计学国际顶级期刊的编委。他是美国AAAS、ASA和IMS的Fellow, 也是ISI的Elected Member。他是1998年ASA的Outstanding Statistical Application Award的共同获奖者；他也是2015年第一个荣获NISS的Alumni Achievement Award的共同获奖者。他在JASA、AoS、JRSSB等统计学国际顶级期刊上发表了很多高质量的论文。