报告题目：Empirical Likelihood Inference over Decentralized Networks

报告人：王启华研究员，中国科学院数学与系统科学研究院

报告时间：2025年11月4日（周二）16:00

报告地点：20-202

报告摘要：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.

报告人简介：王启华，中国科学院数学与系统科学研究院研究员，博士生导师，国家级领军人才，中科院“百人计划”入选者。曾在北京大学、香港大学任教，先后访问加拿大、美国、德国及澳大利亚10多所世界一流大学。主要从事复杂数据经验似然统计推断、缺失数据分析、高维数据统计分析、大规模数据分析等方面的研究，出版专著3部，在The Annals of Statistics, JASA及Biometrika等国际重要刊物发表论文150余篇，部分工作已产生持久不断的学术影响。曾主持国家重点项目、多项面上项目等，作为核心骨干成员先后参加了2项国家自然科学基金创新群体项目及1项国家重点研发计划项目。是高维统计分会理事长，中国现场统计研究会常务理事，中国概率统计学会常务理事，一些国际国内刊物及一些丛书的编委。