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學術動態
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統計學院學術報告預告

發布日期 : 2020-08-25 瀏覽次數 :

報告題目:Optimal Distributed Subsampling for Quasi-likelihood Estimator with Massive Data

報告人:艾明要 教授

報告摘要: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.

報告時間:8月26日10:00-11:00

報告地點:騰會議  ID號 130 876 358

主辦單位:統計學院

報告人簡介:艾明要,北京大學數學科學學院統計學教研室主任、教授、博士生導師。兼任中國數學會理事,中國概率統計學會秘書長,中國現場統計研究會常務理事,試驗設計分會理事長,高維數據統計分會副理事長等。國際重要統計期刊《Statistica Sinica》、《Journal of Statistical Planning and Inference》、《Statistics and Probability Letters》、《Stat》副主編,國內核心期刊 《系統科學與數學》編委,科學出版社《統計與數據科學系列叢書》編委。主要從事試驗設計與分析、大數據分析和應用概率統計的教學和研究工作,在Ann Statist、JASA、Biometrika、《中國科學》等國內外頂尖期刊發表學術論文六十余篇,主持完成國家自然科學基金項目6項,參與完成國家科技部重點研發計劃項目2項。


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