A Cheap Bootstrap Method for Fast Inference
Methodology
2022-02-02 v1 Statistics Theory
Computation
Statistics Theory
Abstract
The bootstrap is a versatile inference method that has proven powerful in many statistical problems. However, when applied to modern large-scale models, it could face substantial computation demand from repeated data resampling and model fitting. We present a bootstrap methodology that uses minimal computation, namely with a resample effort as low as one Monte Carlo replication, while maintaining desirable statistical guarantees. We present the theory of this method that uses a twisted perspective from the standard bootstrap principle. We also present generalizations of this method to nested sampling problems and to a range of subsampling variants, and illustrate how it can be used for fast inference across different estimation problems.
Cite
@article{arxiv.2202.00090,
title = {A Cheap Bootstrap Method for Fast Inference},
author = {Henry Lam},
journal= {arXiv preprint arXiv:2202.00090},
year = {2022}
}