数苑博雅讲座（63）：Penalized Empirical Likelihood over Decentralized Networks
来源：
发布时间：2026-05-29
报告时间：- 2026年4月17日（周五）上午10: 30-11: 30
- 报告地点：
- 国交2栋315会议室
- 报告人：
- 王启华
- 报告人单位：
- 中国科学院数学与系统科学研究院
- 邀请人：
- 左国新
- 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.
