报告题目：Variable importance based interaction modelling with an application on initial spread of COVID-19 in China

报告时间：2026年5月10日下午3点30分

报告地点：北区四号教学楼208室

报告摘要：Interaction selection for linear regression is useful in many fields of modern science, yet very challenging. Existing methods focus on finding one optimal model but they may perform poorly in terms of stability for high-dimensional data, and they do not typically deal with categorical predictors. In this paper, we introduce a variable importance based interaction modelling (VIBIM) procedure for learning interactions in a linear regression model with both continuous and categorical predictors. We apply the VIBIM procedure to a COVID-19 data and show that the VIBIM approach leads to better models in terms of interpretability, stability, reliability, and prediction.

报告人简介：许王莉，中国人民大学教授，博士生导师，中国人民大学吴玉章讲席教授。先后主持5项国家自然科学基金，北京市自然科学基金重点研究专题，教育部人文社会科学重点研究基地重大项目和教育部人文社科基金等多项科研课题。在顶尖期刊JASA, JRSSB, Biometrika, TPAMI等发表百余篇论文。先后入选“新世纪优秀人才计划”和“北京市科技新星计划”，获得中国第十二届北京市统计科研优秀成果奖一等奖（2014），第一届统计科学技术进步二等奖（2021）。

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