A shrinkage approach to improve direct bootstrap resampling under input uncertainty
Year of publication: |
2024
|
---|---|
Authors: | Song, Eunhye ; Lam, Henry ; Barton, Russell R. |
Published in: |
INFORMS journal on computing : JOC ; charting new directions in operations research and computer science ; a journal of the Institute for Operations Research and the Management Sciences. - Linthicum, Md. : INFORMS, ISSN 1526-5528, ZDB-ID 2004082-9. - Vol. 36.2024, 4, p. 1023-1039
|
Subject: | bootstrap resampling | input uncertainty | nonparametric | shrinkage | simulation | Bootstrap-Verfahren | Bootstrap approach | Simulation | Nichtparametrisches Verfahren | Nonparametric statistics | Risiko | Risk | Resampling | Resampling method | Schätztheorie | Estimation theory |
-
Subsampling to enhance efficiency in input uncertainty quantification
Lam, Henry, (2022)
-
On the finite-sample accuracy of nonparametric resampling algorithms for economic time series
Berkowitz, Jeremy, (1999)
-
On the finite-sample accuracy of nonparametric resampling algorithms for economic time series
Berkowitz, Jeremy, (2000)
- More ...
-
Gaussian Markov random fields for discrete optimization via simulation : framework and algorithms
Salemi, Peter L., (2019)
-
Rapid discrete optimization via simulation with Gaussian Markov random fields
Semelhago, Mark, (2021)
-
Uncertainty quantification in vehicle content optimization for General Motors
Song, Eunhye, (2020)
- More ...