A review of distributed statistical inference
Computation
2023-04-14 v1
Abstract
The rapid emergence of massive datasets in various fields poses a serious challenge to traditional 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 are discussed.
Cite
@article{arxiv.2304.06245,
title = {A review of distributed statistical inference},
author = {Yuan Gao and Weidong Liu and Hansheng Wang and Xiaozhou Wang and Yibo Yan and Riquan Zhang},
journal= {arXiv preprint arXiv:2304.06245},
year = {2023}
}