Divide-and-conquer methods for big data analysis

• URL: http://arxiv.org/abs/2102.10771v1
• Date: Mon, 22 Feb 2021 04:40:55 GMT
• Title: Divide-and-conquer methods for big data analysis
• Authors: Xueying Chen, Jerry Q. Cheng, Min-ge Xie
• Abstract summary: Divide-and-conquer methodology refers to a multiple-step process. This article reviews some recently developments of divide-and-conquer methods in a variety of settings.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the context of big data analysis, the divide-and-conquer methodology refers to a multiple-step process: first splitting a data set into several smaller ones; then analyzing each set separately; finally combining results from each analysis together. This approach is effective in handling large data sets that are unsuitable to be analyzed entirely by a single computer due to limits either from memory storage or computational time. The combined results will provide a statistical inference which is similar to the one from analyzing the entire data set. This article reviews some recently developments of divide-and-conquer methods in a variety of settings, including combining based on parametric, semiparametric and nonparametric models, online sequential updating methods, among others. Theoretical development on the efficiency of the divide-and-conquer methods is also discussed.

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