报告题目：A novel approach of empirical likelihood with massive data

报告人：陈夏

时间：10月22日 16：10-17：10

地点：嘟嘟成人网 106会议室

报告摘要：In this work, we propose a novel approach for tackling the obstacles of empirical likelihood in the face of massive data, i.e., split sample mean empirical likelihood (SSMEL), which provides a unique perspective for solving big data problems. We show that the SSMEL estimator has the same estimation efficiency as the empirical likelihood estimator with the full dataset, and maintains the important statistical property of Wilks’ theorem, allowing our proposed approach to be used for statistical inference without estimating the covariance matrix. This effectively tackles the hurdle of the divide and conquer (DC) algorithm for statistical inference. We further illustrate the proposed approach via simulation studies and real data analysis.

报告人简介：陈夏，陕西师范大学数学与嘟嘟成人网 教授、博士生导师。武汉大学概率论与数理统计博士，北京师范大学统计学博士后。兼任陕西省统计学学会副理事长和中国现场统计研究会多个分会的常务理事或理事。主要从事高维数据统计分析和概率极限理论方面的研究。在国内外统计学重要学术期刊发表论文40余篇，在科学出版社出版专著1部、教材1部。主持国家级和省部级项目多项。获陕西省高等教育优秀教材一等奖、陕西省学位与研究生教育成果奖一等奖和陕西高校科学技术奖二等奖等奖励多项。