Shenzhen University School of Mathematical Sciences  

Liyuan Scholars Colloquium Session 162

Title:  Penalized Empirical Likelihood Inference over Decentralized Networks

Speaker: Professor Qihua Wang (Academy of Mathematics and Systems Science, Chinese Academy of Sciences)

Time: 10:20–11:20, March 24, 2026

Location: Room 3, Huixing Building, Yuehai Campus, Shenzhen University

Abstract:  As a nonparametric statistical inference approach, empirical likelihood has been found very useful in numerous occasions.

However, it encounters serious computational challenges when applied directly to the modern massive dataset. This article studies empirical likelihood inference over decentralized distributed networks, where the data are locally collected and stored by different nodes. To fully utilize the data, this article fuses Lagrange multipliers calculated in different nodes by employing a penalization technique. The proposed distributed empirical log-likelihood ratio statistic with Lagrange multipliers solved by the penalized function is asymptotically standard chi-squared under regular conditions even for a divergent machine number. Nevertheless, the optimization problem with the fused penalty is still hard to solve in the decentralized distributed network. To address the problem, two alternating direction method of multipliers (ADMM) based algorithms are proposed, which both have simple node-based implementation schemes. Theoretically, this article establishes convergence properties for proposed algorithms, and further proves the linear convergence of the second algorithm in some specific network structures. The proposed methods are evaluated by numerical simulations and illustrated with analyses of census income and Ford gobike datasets.

Speaker Profile:  Qihua Wang is a Research Fellow at the Academy of Mathematics and Systems Science, Chinese Academy of Sciences (CAS), a doctoral advisor, a recipient of the National Science Fund for Distinguished Young Scholars, and a fellow of the CAS “Hundred Talents Program.” He has taught at Peking University and the University of Hong Kong, and has visited more than 10 world-class universities in Canada, the United States, Germany, and Australia. His research focuses on empirical likelihood statistical inference for complex data, missing data analysis, statistical analysis of high-dimensional data, and large-scale data analysis. He has published three monographs and over 150 papers in leading international journals such as the Journal of the Royal Statistical Society Series B(JRSSB), The Annals of Statistics, the Journal of the American Statistical Association (JASA), and Biometrika. Some of his work has had a lasting and significant academic impact. He has led projects funded by the National Science Fund for Distinguished Young Scholars, the National Natural Science Foundation of China’s Key Projects, and multiple General Projects. As a core member, he has participated in two National Natural Science Foundation of China Innovation Group Projects and one National Key Research and Development Program project.

All faculty and students are welcome!  

School of Mathematical Sciences  

March 20, 2026