The Big Data Bootstrap

TL;DR

The paper introduces the Bag of Little Bootstraps (BLB), a computationally efficient method for estimator assessment suitable for large datasets and parallel computing, maintaining bootstrap's statistical properties.

Contribution

It proposes BLB, combining bootstrap and subsampling, with extensive empirical and theoretical validation for large-scale data analysis.

Findings

BLB is statistically correct and reliable.

BLB performs well on large datasets and in parallel computing environments.

Hyperparameter selection for BLB is effective and practical.

Abstract

The bootstrap provides a simple and powerful means of assessing the quality of estimators. However, in settings involving large datasets, the computation of bootstrap-based quantities can be prohibitively demanding. As an alternative, we present the Bag of Little Bootstraps (BLB), a new procedure which incorporates features of both the bootstrap and subsampling to obtain a robust, computationally efficient means of assessing estimator quality. BLB is well suited to modern parallel and distributed computing architectures and retains the generic applicability, statistical efficiency, and favorable theoretical properties of the bootstrap. We provide the results of an extensive empirical and theoretical investigation of BLB's behavior, including a study of its statistical correctness, its large-scale implementation and performance, selection of hyperparameters, and performance on real data.