主题：Mixture Conditional Regression with Ultrahigh Dimensional Text Data for Estimating Extralegal Factor Effects

报告人：Hansheng WANG, Professor, Guanghua School of Management, Peking University

主持人：Yongtao GUAN, Presidential Chair Professor, School of Data Science, CUHK-Shenzhen

日期：10 May (Friday), 2024

时间：11:00 AM - 12:00 PM, Beijing Time

形式：Hybrid

地点：103 Meeting Room, Daoyuan Building

SDS视频号直播：

语言：English

摘要：

Testing judicial impartiality is a problem of fundamental importance in empirical legal studies, for which standard regression methods have been popularly used to estimate the extralegal factor effects. However, those methods cannot handle control variables with ultrahigh dimensionality, such as those found in judgment documents recorded in text format. To solve this problem, we develop a novel mixture conditional regression (MCR) approach, assuming that the whole sample can be classified into a number of latent classes. Within each latent class, a standard linear regression model can be used to model the relationship between the response and a key feature vector, which is assumed to be of a fixed dimension. Meanwhile, ultrahigh dimensional control variables are then used to determine the latent class membership, where a naive Bayes type model is used to describe the relationship. Hence, the dimension of control variables is allowed to be arbitrarily high. A novel expectation-maximization algorithm is developed for model estimation. Therefore, we are able to estimate the key parameters of interest as efficiently as if the true class membership were known in advance. Simulation studies are presented to demonstrate the proposed MCR method. A real dataset of Chinese burglary offenses is analyzed for illustration purposes.

简介：

Professor Hansheng Wang is from the department of Business Statistics and Econometrics at Guanghua School of Management, Peking university. He is the winner of the National Outstanding Youth Fund and is the Changjiang Distinguished Professor of the Ministry of Education. He is the founding president of the Youth Statistician Association of the National Industrial Statistics Teaching and Research Association. He is a Fellow of the Institute of Mathematical Statistics (IMS), the American Statistical Association (ASA), and an Elected Member of the International Statistical Institute (ISI). He has served as associate editor or editor for 9 international academic journals. He has published more than 100 papers so far, in addition to a monograph and 4 textbooks. He has been recognized as Elsevier China Highly Cited Scholar (Mathematics: 2014-2018; Applied Economics, 2020; Statistics, 2021–2022).