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.
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).
