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      大數據分析中的分布式抽樣技術

      發布者:文明辦發布時間:2020-12-15瀏覽次數:10


      主講人:艾明要  北京大學教授


      時間:2020年12月18日14:00


      地點:3號樓332


      舉辦單位:數理學院


      主講人介紹: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。兼任中國概率統計學會秘書長,中國數學會均勻設計分會副主任,中國現場統計研究會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面上項目5項、重點項目子課題1項,參與完成國家科技部重點研發計劃項目2項。  


      內容介紹: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.

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