The goal of this software is to demonstrate the shrinkage bootstrap methods for input uncertainty quantification proposed in "A Shrinkage Approach to Improve Direct Bootstrap Resampling under Input Uncertainty" by Eunhye Song, Henry Lam, and Russell Barton accepted for publication at INFORMS Journal on Computing.
To cite the contents of this repository, please cite both the paper and this repo, using their respective DOIs.
Below is the BibTex for citing this snapshot of the repository.
@article{SongLamBarton2023,
author = {Eunhye Song and Henry Lam and Russell R. Barton},
publisher = {INFORMS Journal on Computing},
title = {A Shrinkage Approach to Improve Direct Bootstrap Resampling under Input Uncertainty},
year = {2023},
doi = {https://doi.org/10.1287/ijoc.2022.0044},
note = {Source codes are available for download at https://github.com/INFORMSJoC/2022.0044}
}
To replicate the results in Sections 6.1--6.3, run the scripts in the 'scripts' folder. They are also duplicated in 'src.'
For support in using this software, please email Eunye Song (eunhye.song@isye.gatech.edu).