讲座时间：2025年11月27日(周四) 14:30-15:30
地点：综合楼644会议室
报告题目：Statistical Analysis of Weak Signals
报告人简介：
Dr. Song is Professor of Biostatistics at the University of Michigan School of Public Health, Ann Arbor. He received his PhD in Statistics from the University of British Columbia, Vancouver, Canada in 1996. He has published over 250 peer-reviewed papers and graduated 28 PhD students and trained 6 postdoc research fellows. Dr. Song's current research interests include data integration, distributed inference, high dimensional data analysis, longitudinal data analysis, mediation analysis, and spatiotemporal modeling. He is IMS Fellow, ASA Fellow and Elected Member of the International Statistical Institute. Dr. Song now serves as Area Editor of the Annals of Applied Statistics (Medicine, EHR and Smart Health), Associate Editor of the Journal of American Statistical Association, Journal of the Royal Statistical Society Series B (Statistical Methodology) and the Journal of Multivariate Analysis.
报告摘要：
The statistical analysis of weak signals (SAWS) is a fundamental challenge in various practical domains, including questionnaire items, agrochemical residues in food, genetic variants in DNA, daily physical activity, and virus detection in wastewater. In regression analysis, identifying individual associations of weak signals is often difficult due to limited sample sizes. As a result, signals are frequently grouped into bundles to enhance detectability. Supervised homogeneity pursuit is a popular approach for forming such bundles to achieve stronger associations with outcomes of interest. Recently, we proposed a novel SAWS framework that leverages mixed- integer optimization to simultaneously perform bundle formation, association estimation, and inference. A technical innovation pertains to the reformulation of a grouping/clustering analysis as an estimation problem. This talk will discuss both the theoretical foundations and numerical performance of this approach.
