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Subdata selection for subgroup analysis

发布时间:2019-11-22 作者: 浏览次数:
Speaker: Min Yang DateTime: 2019年11月27日(星期三)上午10:30—11:30
Brief Introduction to Speaker:

Prof.  Min Yang,University of Illinois at Chicago。

Place: 6号楼二楼报告厅
Abstract: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. While subgroup analysis addresses the balance of the model complexity and interpretability efficiently, one disadvantage of this approach is the computation complexity. Even when the sample size is moderate, it will take a considerate computation resource to analyze the data. How to select informative subdata under subgroup analysis? In this talk, a new framework is proposed to address this issue.