A review of distributed statistical inference  

作　　者：Yuan Gao Weidong Liu Hansheng Wang Xiaozhou Wang Yibo Yan Riquan Zhang 

机构地区：[1]School of Statistics and Key Laboratory of Advanced Theory and Application in Statistics and Data Science–MOE,East China Normal University,Shanghai,People’s Republic of China [2]School of Mathematical Sciences and Key Lab of Articial Intelligence–MOE,Shanghai Jiao Tong University,Shanghai,People’s Republic of China [3]Guanghua School of Management,Peking University,Beijing,People’s Republic of China

出　　处：《Statistical Theory and Related Fields》2022年第2期89-99,共11页统计理论及其应用（英文）

基　　金：This work is supported by National Natural Science Foun-dation of China(No.11971171);the 111 Project(B14019)and Project of National Social Science Fund of China(15BTJ027);Weidong Liu’s research is supported by National Program on Key Basic Research Project(973 Program,2018AAA0100704);National Natural Science Foundation of China(No.11825104,11690013);Youth Talent Sup-port Program,and a grant from Australian Research Council.Hansheng Wang’s research is partially supported by National Natural Science Foundation of China(No.11831008,11525101,71532001);It is also supported in part by China’s National Key Research Special Program(No.2016YFC0207704).

摘　　要：The rapid emergence of massive datasets in various fields poses a serious challenge to tra-ditional statistical methods.Meanwhile,it provides opportunities for researchers to develop novel algorithms.Inspired by the idea of divide-and-conquer,various distributed frameworks for statistical estimation and inference have been proposed.They were developed to deal with large-scale statistical optimization problems.This paper aims to provide a comprehensive review for related literature.It includes parametric models,nonparametric models,and other frequently used models.Their key ideas and theoretical properties are summarized.The trade-off between communication cost and estimate precision together with other concerns is discussed.

关 键 词：Distributed computing DIVIDE-AND-CONQUER communication-efficiency shrinkage methods nonparametric estimation principal component analysis feature screening BOOTSTRAP 

分 类 号：O21[理学—概率论与数理统计]