The Library
Optimal subsampling bootstrap for massive data
Tools
Ma, Yingying, Leng, Chenlei and Wang, Hansheng (2023) Optimal subsampling bootstrap for massive data. Journal of Business & Economic Statistics, 42 (1). pp. 174-186. doi:10.1080/07350015.2023.2166514 ISSN 0735-0015.
|
PDF
Optimal Subsampling Bootstrap for Massive Data.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1578Kb) | Preview |
|
PDF
WRAP-optimal-subsampling-bootstrap-massive-data-Leng-2022.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (1496Kb) |
Official URL: https://doi.org/10.1080/07350015.2023.2166514
Abstract
The bootstrap is a widely used procedure for statistical inference because of its simplicity and attractive statistical properties. However, the vanilla version of bootstrap is no longer feasible computationally for many modern massive datasets due to the need to repeatedly resample the entire data. Therefore, several improvements to the bootstrap method have been made in recent years, which assess the quality of estimators by subsampling the full dataset before resampling the subsamples. Naturally, the performance of these modern subsampling methods is influenced by tuning parameters such as the size of subsamples, the number of subsamples, and the number of resamples per subsample. In this paper, we develop a novel hyperparameter selection methodology for selecting these tuning parameters. Formulated as an optimization problem to find the optimal value of some measure of accuracy of an estimator subject to computational cost, our framework provides closed-form solutions for the optimal hyperparameter values for subsampled bootstrap, subsampled double bootstrap and bag of little bootstraps, at no or little extra time cost. Using the mean square errors as a proxy of the accuracy measure, we apply our methodology to study, compare and improve the performance of these modern versions of bootstrap developed for massive data through numerical study. The results are promising.
Item Type: | Journal Article | ||||||
---|---|---|---|---|---|---|---|
Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||
Journal or Publication Title: | Journal of Business & Economic Statistics | ||||||
Publisher: | Taylor & Francis | ||||||
ISSN: | 0735-0015 | ||||||
Official Date: | 12 January 2023 | ||||||
Dates: |
|
||||||
Volume: | 42 | ||||||
Number: | 1 | ||||||
Page Range: | pp. 174-186 | ||||||
DOI: | 10.1080/07350015.2023.2166514 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 8 December 2022 | ||||||
Date of first compliant Open Access: | 12 January 2024 | ||||||
Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year