报告题目： New Development in  Empirical Likelihood

报告时间：2025年3月6日  （周四）15：00  

报告地点：综合楼644会议室

报告人：Jiahua Chen

报告人简介：

Professor Jiahua Chen, Fellow of the Royal Society of Canada (RSC) and Canada Research Chair (Tier I) in Statistics, is a distinguished statistician and faculty member in the Department of Statistics at the University of British Columbia. His research spans finite mixture models, statistical genetics, empirical likelihood, survey methodology, and experimental design.  

He has made fundamental contributions to statistical methodology, including introducing empirical likelihood to survey sampling, inventing the EM test for finite mixture models, and developing the extended Bayesian information criterion (EBIC) for variable selection in large model spaces.  

In recognition of his impact, Professor Chen was elected a Fellow of the Royal Society of Canada in 2022, one of the highest honours in Canadian academia. He is also a Fellow of the Institute of Mathematical Statistics and the American Statistical Association.  

His work has earned him numerous prestigious awards, including the CRM-SSC Prize in Statistics and the Gold Medal of the Statistical Society of Canada in 2014, the SSC's highest honor. In 2016, he received the International Chinese Statistical Association Distinguished Achievement Award.

报告摘要：

We present a collection of interconnected methodologies for conducting non-parametric and semi-parametric statistical inference. While maximum likelihood estimation (MLE) is widely valued in parametric models for its strong consistency and optimality, these benefits weaken in cases of model misspecification or non-regularity. Moreover, the optimal properties of parametric MLE are established only for a local maximum of the likelihood function within a small neighborhood of the true parameter values, which is not fully satisfactory. Ideally, achieving a globally consistent maximum would be preferable.

To address the risks associated with model misspecification, one can adopt a semi-parametric framework and employ estimating functions within the empirical likelihood (EL) paradigm. However, the fundamental issue of MLE being merely a local maximum in the EL framework remains. In this work, we establish a set of clear conditions that guarantee the global consistency of the maximum. We propose a "global maximum test" to assess whether a given local maximum is indeed the global solution. Additionally, we introduce a "global maximum remedy," which improves global consistency by expanding the set of estimating functions within the EL framework.