报告人：Gongjun Xu
报告地点：数学与统计学院415室
报告时间：2019年07月08日星期一10:00-11:00
邀请人：高巍
报告摘要：
Latent class models have wide applications in social and biological sciences. In many applications, pre-specified restrictions are imposed on the parameter space of latent class models, through a design matrix, to reflect practitioners' diagnostic assumptions about how the observed responses depend on the respondents' latent traits. Though widely used in various fields, such restricted latent class models suffer from nonidentifiability due to the models' discrete nature and complex restricted structure. This work addresses the fundamental identifiability issue of restricted latent class models by developing a general framework for strict and partial identifiability of the model parameters. The developed identifiability conditions only depend on the design matrix and are easily checkable, which provides useful practical guidelines for designing statistically valid diagnostic tests. Furthermore, the new theoretical framework is applied to establish, for the first time, identifiability of several designs from cognitive diagnosis applications.
主讲人简介：
Dr. Gongjun Xu is an assistant professor of Statistics at the University of Michigan. He received his B.S. in Statistics from the University of Science and Technology of China in 2008 and his Ph.D. in Statistics from Columbia University in 2013. His research interests include latent variable models, psychometrics, cognitive diagnosis modeling, high-dimensional statistics, and semiparametric statistics. He received NSF CAREER Award 2019, International Chinese Statistical Association (ICSA) Outstanding Young Researcher Award 2019, Bernoulli Society New Researcher Award in 2019, etc.
