報告人:許王莉
時 間: 2021年11月18日14:30-15:30
地 點:騰訊會議 292955018
題 目: Variable Importance Based Interaction Modeling On Initial Spread Of COVID-19 In CHINA
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.
報告人簡介：
許王莉，中國人民大學明理書院副院長，統計必一教授，博士生導師， 近年來一直從事模型擬合優度檢驗，高維數據分析，隨機缺失數據，兩階段抽樣數據以及縱向數據分析等方面的統計推斷研究。先後主持了4項國家自然科學基金，以及教育部人文社會科學重點研究基地重大項目，北京市自然科學基金重點項目和教育部人文社科基金等多項科研課題, 在統計學國際一流期刊（包括頂級期刊）發表論文70余篇，並在科學出版社合作出版《非參數蒙特卡洛檢驗及其應用》和單著《缺失數據的模型檢驗及其應用》。