报告题目：Optimal Distributed Subsampling for Quasi-likelihood Estimator with Massive Data

主 讲 人：艾明要 教授

单 位：北京大学

时 间：12月3日10:00

地 点：公司二楼会议室

腾 讯 ID：221 416 310

摘 要：

Nonuniform subsampling methods are effective to reduce computational burden and maintain estimation efficiency for massive data. Existing methods mostly focus on subsampling with replacement due to its high computational efficiency. If the data volume is so large that nonuniform subsampling probabilities cannot be calculated all at once, then subsampling with replacement is infeasible to implement. This paper solves this problem using Poisson subsampling. We first derive optimal Poisson subsampling probabilities in the context of quasi-likelihood estimation under the A- and L-optimality criteria. For a practically implementable algorithm with approximated optimal subsampling probabilities, we establish the consistency and asymptotic normality of the resultant estimators. To deal with the situation that the full data are stored in different blocks or at multiple locations, we develop a distributed subsampling framework, in which statistics are computed simultaneously on smaller partitions of the full data. Asymptotic properties of the resultant aggregated estimator are investigated. We illustrate and evaluate the proposed strategies through numerical experiments on simulated and real data sets.

简 介：

艾明要，北京大学数学科学学院统计学教授、博士生导师。兼任中国现场统计研究会第十一届理事会副理事长，试验设计分会理事长，高维数据统计分会副理事长，中国数学会第十三届理事会理事，中国概率统计学会秘书长，中国数学会均匀设计分会副主任等。担任4个国际重要SCI期刊Statistica Sinica、JSPI、SPL和Stat的副主编，国内核心期刊 《系统科学与数学》编委，科学出版社《统计与数据科学丛书》编委。主要从事大数据采样技术、试验设计与分析、应用概率统计的教学和研究工作，在Ann Statist、JASA、Biometrika、《中国科学》等国内外重要期刊发表学术论文七十余篇。主持国家自然科学基金重点项目“大数据采样技术与统计设计理论研究”1项，主持国家自然科学基金面上项目6项，参与完成科技部重点研发计划(973)项目2项。