主讲人简介：Min Yang 教授University of Illinois at Chicago
研究领域：Design and analysis of experiment, linear and nonlinear models, medical and pharmaceutical studies.
在试验设计领域取得了重要成果，在Ann. Statist.和J. Amer. Statist. Assoc.等期刊发表系列文章。
How to implement data reduction to draw useful information from big data is a hot spot of modern scientific research. One attractive approach is data reduction through subdata selection. Typically, this approach is based on some strong model assumption: data follows one specific statistical model. Big data is complexity and it may not be the best to model the data using one specific model. Instead of assuming one specific model for all population, subgroup analysis assumes there is a hidden group structure and each group has its own model. How to select informative subdata under subgroup analysis? In this talk, a new framework is proposed to address this issue.