题目：Renewable Estimation and Incremental Inference in Generalized Linear Models

主讲人：密西根大学 宋学坤教授

主持人：西南财经大学统计学院 常晋源教授

时间：2020年7月14日（周二）10:00-11:20

直播平台及会议ID：Zoom，976 3253 1414

报告摘要：

I will present a new online learning algorithm to analyze streaming data using the generalized linear models. Our proposed method is developed within a new framework of renewable estimation, in which the maximum likelihood estimation can be renewed with current data and summary statistics of historic data, but with no use of any historic data themselves. In the implementation, we design a new data flow, called the Rho architecture to accommodate the data storage of current and historic data, as well as to communicate with the computing layer of the system in order to facilitate sequential learning. We prove both estimation consistency and asymptotic normality of the renewable MLE, and propose some sequential inferences for model parameters. We illustrate our methods by numerical examples from both simulation  experiments and real-world analysis.

主讲人简介：

Dr. Song is Professor of Biostatistics, and Associate Chair of Research, at the Department of Biostatistics, School of Public Health in the University of Michigan, Ann Arbor, since January, 2008.  He received his PhD in Statistics from the University of British Columbia, Vancouver, Canada in 1996.  He has published over 170 peer-reviewed papers.  Dr. Song's research interests include distributed inference, high-dimensional data analysis, longitudinal data analysis, missing data problems, spatiotemporal modeling, and statistical methods in precision health. He is ASA Fellow and Elected Member of the International Statistical Institute. Dr. Song now serves as Associate Editor of the Journal of American Statistical Association, the Canadian Journal of Statistics, and the Journal of Multivariate Analysis.