报告题目：Optimal Subsampling Algorithm for Big Data Regression
报 告 人：艾明要 教授 北京大学
报告时间：2019年11月21日上午10：00-11：00
报告地点：数学楼第二报告厅
报告摘要：
To fast approximate the MLE with massive data, this paper studies the optimal subsampling method under the A-optimality criterion for generalized linear models (GLM). The consistency and asymptotic normality of the estimator from a general subsampling algorithm are established, and optimal subsampling probabilities under the A- and L-optimality criteria are derived. Furthermore, using Frobenius norm matrix concentration inequality, finite sample properties of the subsample estimator based on optimal subsampling probabilities are also derived. Since the optimal subsampling probabilities depend on the full data estimate, an adaptive two-step algorithm is developed. Asymptotic normality and optimality of the estimator from this adaptive algorithm are established. The proposed methods are illustrated and evaluated through numerical experiments on simulated and real datasets.
报告人简介：
艾明要，北京大学数学科学学院统计学教研室主任、教授、博士生导师。兼任中国数学会概率统计学会秘书长，中国现场统计研究会常务理事，试验设计分会理事长，高维数据统计分会副理事长等。国际重要统计期刊《Statistica Sinica》、《Journal of Statistical Planning and Inference》、《Statistics and Probability Letters》、《STAT》副主编，国内核心期刊 《系统科学与数学》编委，科学出版社《统计与数据科学系列丛书》编委。主要从事试验设计与分析、计算机试验、大数据分析和应用统计的教学和研究工作，在Ann Statist、JASA、Biometrika、Technometrics、Statist Sinica等国内外顶尖期刊发表学术论文六十余篇，主持完成多项国家自然科学基金面上项目和重点项目子课题，参与完成国家科技部973课题2项。
