This paper presents a selective review of statistical computation methods for massive data analysis.A huge amount of statistical methods for massive data computation have been rapidly developed in the past decades.In ...This paper presents a selective review of statistical computation methods for massive data analysis.A huge amount of statistical methods for massive data computation have been rapidly developed in the past decades.In this work,we focus on three categories of statistical computation methods:(1)distributed computing,(2)subsampling methods,and(3)minibatch gradient techniques.The first class of literature is about distributed computing and focuses on the situation,where the dataset size is too huge to be comfortably handled by one single computer.In this case,a distributed computation system with multiple computers has to be utilized.The second class of literature is about subsampling methods and concerns about the situation,where the blacksample size of dataset is small enough to be placed on one single computer but too large to be easily processed by its memory as a whole.The last class of literature studies those minibatch gradient related optimization techniques,which have been extensively used for optimizing various deep learning models.展开更多
基金supported by the National Natural Science Foundation of China[grant numbers 72301070,72171226,12271012,12171020,12071477,72371241,72222009,71991472 and 12331009]the National Statistical Science Research Project[grant number 2023LD008]+3 种基金the Fundamental Research Funds for the Central Universities in UIBE[grant number CXTD13-04]the MOE Project of Key Research Institute of Humanities and Social Sciences[grant number 22JJD110001]the Program for Innovation Research,the disciplinary funding and the Emerging Interdisciplinary Project of Central University of Finance and Economicsthe Postdoctoral Fellowship Program of CPSF[grant numbers GZC20231522,GZC20230111 and GZB20230070].
文摘This paper presents a selective review of statistical computation methods for massive data analysis.A huge amount of statistical methods for massive data computation have been rapidly developed in the past decades.In this work,we focus on three categories of statistical computation methods:(1)distributed computing,(2)subsampling methods,and(3)minibatch gradient techniques.The first class of literature is about distributed computing and focuses on the situation,where the dataset size is too huge to be comfortably handled by one single computer.In this case,a distributed computation system with multiple computers has to be utilized.The second class of literature is about subsampling methods and concerns about the situation,where the blacksample size of dataset is small enough to be placed on one single computer but too large to be easily processed by its memory as a whole.The last class of literature studies those minibatch gradient related optimization techniques,which have been extensively used for optimizing various deep learning models.
