A Shrinkage Approach to Improve Direct Bootstrap Resampling under Input Uncertainty
INFORMS JOURNAL ON COMPUTING（2024）
Georgia Inst Technol | Columbia Univ | Penn State Univ
Abstract
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.
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Key words
bootstrap resampling,input uncertainty,nonparametric,simulation,shrinkage
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