Title (题目): Latent Variable and Network Models for Measurement
Time (时间): 4pm-5pm, 2015-11-16 (Monday)
Location (地点): 伟清楼209 (Center for Statistical Science, Tsinghua University)
Speaker (报告人): Jingchen Liu, Columbia University
Abstract (摘要):
One of the main tasks of statistical models is to characterize the dependence structures of multi-dimensional distributions. Latent variable model takes advantage of the fact that the dependence of a high dimensional random vector is often induced by just a few latent (unobserved) factors. In this talk, we present several problems regarding latent variable models in the context of measurement theory. When the dimension grows higher and the dependence structure becomes more complicated, it is hardly possible to find a low dimensional parametric latent variable model that fits well. We further enrich the model by including a network structure on top of the latent structure. Thus, the main variation of the random vector remains governed by latent variables and the network captures the remainder dependence. Both have interpretations in practice.
About the speaker (报告人介绍)
Jingchen Liu received his Ph.D. in Statistics from Harvard University in 2008. He is now an associate professor in statistics at Columbia University. He is the winner of 2013 Tweedie New Researcher Award and co-winner of 2009 Best Publication in Applied Probability Award.