A review of distributed statistical inference
arXiv:2304.06245 · doi:10.1080/24754269.2021.1974158
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.
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Cited by in corpus (5)
- Federated and distributed learning applications for electronic health records and structured medical data: A scoping review
- Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches
- Distributed Estimation and Inference for Semi-parametric Binary Response Models
- Data splitting improves statistical performance in overparametrized regimes
- Grid Point Approximation for Distributed Nonparametric Smoothing and Prediction