报告人：许王莉
报告地点：腾讯会议ID:308 351 882
报告时间：2021年12月29日星期三10:00-11:00
报告摘要：
Interaction selection for linear regression models with categorical predictors is useful in many fields of modern science, yet very challenging when the number of predictors is large. Existing interaction selection methods focus on finding one optimal model. While attractive properties such as consistency and oracle property have been well established for such methods, they actually may perform poorly in terms of stability for high-dimensional data, and they do not deal with categorical predictors. In this paper, we introduce a variable importance based interaction modeling (VIBIM) procedure for learning interactions in a linear regression model with both continuous and categorical predictors. It delivers multiple strong candidate models with high stability and interpretability. We apply the VIBIM procedure to a Corona Virus Disease 2019 (COVID-19) data used in Tian et al. (2020) and measure the effects of relevant factors, including transmission control measures on the spread of COVID-19. We show that the VIBIM approach leads to better models in terms of interpretability, stability, reliability and prediction, compared to the models in Tian et al. (2020) and by some group variable selection methods.
主讲人简介：
许王莉，中国人民大学明理书院副院长，统计学院教授，博士生导师， 近年来一直从事模型拟合优度检验，高维数据分析，随机缺失数据，两阶段抽样数据以及纵向数据分析等方面的统计推断研究。先后主持了4项国家自然科学基金，以及教育部人文社会科学重点研究基地重大项目，北京市自然科学基金重点项目和教育部人文社科基金等多项科研课题, 在统计学国际一流期刊（包括顶级期刊）发表论文70余篇，并在科学出版社合作出版《非参数蒙特卡洛检验及其应用》和单著《缺失数据的模型检验及其应用》。
