题目：Distributed Empirical Likelihood Inference with Massive Data
报告人： 王启华
报告时间：2024年3月20日 14:30-15:30
地点：综合楼644会议室
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，国家高层次领军人才。曾在北京大学、香港大学任教及在深圳大学与浙江工商大学任特聘教授，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，出版专著三部，在The Annals of Statistics, JASA及Biometrika等国际重要刊物发表论文140余篇，部分工作已产生持久的学术影响。曾主持国家自然科学重点项目、多项面上项目，作为核心骨干成员先后参加了两项国家自然科学基金创新群体项目。是高维统计分会理事长，生存分析分会副理事长，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，曾任或现任《中国科学》（中英文版）(2005-2012)、Electronic Research Archive、Ann. Inst. Stat. Math、Biostatistics & Epidemiology及《应用数学学报》英文版等刊物及《现代数学基础丛书》与《统计与数据科学丛书》的编委。
报告摘要：
Empirical likelihood is a very important nonparametric approach which is of wide application. However, it is hard and even infeasible to calculate the empirical log-likelihood ratio statistic with massive data. The main challenge is the calculation of the Lagrange multiplier. This motivates us to develop a distributed empirical likelihood method by calculating the Lagrange multiplier in a multi-round distributed manner. It is shown that the distributed empirical log-likelihood ratio statistic is asymptotically standard chi-squared under some mild conditions. The proposed algorithm is communication-efficient and achieves the desired accuracy in a few rounds. Further, the distributed empirical likelihood method is extended to the case of Byzantine failures. A machine selection algorithm is developed to identify the worker machines without Byzantine failures such that the distributed empirical likelihood method can be applied. The proposed methods are evaluated by numerical simulations and illustrated with an analysis of airline on-time performance study and a surface climate analysis of Yangtze River Economic Belt.
