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陕西师范大学陈夏教授学术报告预告
- 来源：
- 学校官网
- 收录时间：
- 2026-07-10 03:12:47
- 时间：
- 2026-07-13 15:00:00
- 地点：
- 文渊楼 B536
- 报告人：
- 陈夏
- 学校：
- 山东师范大学
- 关键词：
- empirical likelihood, massive data, SSMEL, statistical inference, Wilks' theorem, high-dimensional data
- 简介：
- 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.
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报告介绍：
报告题目：A novel approach of empirical likelihood with massive data
报 告 人：陈夏，陕西师范大学
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.
报告人介绍：
陈夏，陕西师范大学教授、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。先后主持国家自然科学基金重点项目子课题、国家自然科学基金、教育部人文社科基金和陕西省自然科学基金等项目多项。曾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。
报告图片：
