Article

Resampling methods for input modeling

Authors:

Russell R. Barton,

Lee W. SchrubenAuthors Info & Claims

WSC '01: Proceedings of the 33nd conference on Winter simulation

Pages 372 - 378

Published: 09 December 2001 Publication History

Abstract

Stochastic simulation models are used to predict the behavior of real systems whose components have random variation. The simulation model generates artificial random quantities based on the nature of the random variation in the real system. Very often, the probability distributions occurring in the real system are unknown, and must be estimated using finite samples. This paper shows three methods for incorporating the error due to input distributions that are based on finite samples, when calculating confidence intervals for output parameters.

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Cited By

He LSong EFeng B(2023)Efficient Input Uncertainty Quantification for Regenerative SimulationProceedings of the Winter Simulation Conference10.5555/3643142.3643174(385-396)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3643142.3643174
Chen MLiu ZLam H(2022)Distributional Input UncertaintyProceedings of the Winter Simulation Conference10.5555/3586210.3586430(2617-2628)Online publication date: 11-Dec-2022
https://dl.acm.org/doi/10.5555/3586210.3586430
Lam H(2022)Cheap Bootstrap for Input Uncertainty QuantificationProceedings of the Winter Simulation Conference10.5555/3586210.3586404(2318-2329)Online publication date: 11-Dec-2022
https://dl.acm.org/doi/10.5555/3586210.3586404
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Index Terms

Resampling methods for input modeling

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Information & Contributors

Information

Published In

cover image ACM Conferences

WSC '01: Proceedings of the 33nd conference on Winter simulation

December 2001

1595 pages

ISBN:078037309X

Conference Chair:
Matt Rohrer
Brooks Automation, Bountiful, Utah
,
Program Chair:
Deb Medeiros
Penn State University, University Park, PA
,
Publications Chair:
Mark Grabau
Accenture, Reston, VA

Sponsors

IIE: Institute of Industrial Engineers
INFORMS/CS: Institute for Operations Research and the Management Sciences/College on Simulation
ASA: American Statistical Association
ACM: Association for Computing Machinery
SIGSIM: ACM Special Interest Group on Simulation and Modeling
IEEE/CS: Institute of Electrical and Electronics Engineers/Computer Society
NIST: National Institute of Standards and Technology
IEEE/SMCS: Institute of Electrical and Electronics Engineers/Systems, Man, and Cybernetics Society
SCS: The Society for Computer Simulation International

Publisher

IEEE Computer Society

United States

Publication History

Published: 09 December 2001

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WSC01

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IIE
INFORMS/CS
ASA
ACM
SIGSIM
IEEE/CS
NIST
IEEE/SMCS
SCS

WSC01: Winter Simulation Conference 2001

December 9 - 12, 2001

Virginia, Arlington

Acceptance Rates

WSC '01 Paper Acceptance Rate 111 of 155 submissions, 72%;

Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

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Citations

Cited By

He LSong EFeng B(2023)Efficient Input Uncertainty Quantification for Regenerative SimulationProceedings of the Winter Simulation Conference10.5555/3643142.3643174(385-396)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3643142.3643174
Chen MLiu ZLam H(2022)Distributional Input UncertaintyProceedings of the Winter Simulation Conference10.5555/3586210.3586430(2617-2628)Online publication date: 11-Dec-2022
https://dl.acm.org/doi/10.5555/3586210.3586430
Lam H(2022)Cheap Bootstrap for Input Uncertainty QuantificationProceedings of the Winter Simulation Conference10.5555/3586210.3586404(2318-2329)Online publication date: 11-Dec-2022
https://dl.acm.org/doi/10.5555/3586210.3586404
Wang HNg SZhang X(2020)A gaussian process based algorithm for stochastic simulation optimization with input distribution uncertaintyProceedings of the Winter Simulation Conference10.5555/3466184.3466518(2899-2910)Online publication date: 14-Dec-2020
https://dl.acm.org/doi/10.5555/3466184.3466518
Lam HZhang J(2020)Distributionally constrained stochastic gradient estimation using noisy function evaluationsProceedings of the Winter Simulation Conference10.5555/3466184.3466233(445-456)Online publication date: 14-Dec-2020
https://dl.acm.org/doi/10.5555/3466184.3466233
Feng BSong ESon YHaas P(2019)Efficient input uncertainty quantification via green simulation using sample path likelihood ratiosProceedings of the Winter Simulation Conference10.5555/3400397.3400697(3693-3704)Online publication date: 8-Dec-2019
https://dl.acm.org/doi/10.5555/3400397.3400697
Huang ZLam HSon YHaas P(2019)On the impacts of tail model uncertainty in rare-event estimationProceedings of the Winter Simulation Conference10.5555/3400397.3400472(950-961)Online publication date: 8-Dec-2019
https://dl.acm.org/doi/10.5555/3400397.3400472
Barton RLam HSong EJohansson BJain S(2018)Revisiting direct bootstrap resampling for input model uncertaintyProceedings of the 2018 Winter Simulation Conference10.5555/3320516.3320716(1635-1645)Online publication date: 9-Dec-2018
https://dl.acm.org/doi/10.5555/3320516.3320716
Lam HQian HJohansson BJain S(2018)Subsampling variance for input uncertainty quantificationProceedings of the 2018 Winter Simulation Conference10.5555/3320516.3320713(1611-1622)Online publication date: 9-Dec-2018
https://dl.acm.org/doi/10.5555/3320516.3320713
Zhou ELiu TJohansson BJain S(2018)Online quantification of input uncertainty for parametric modelsProceedings of the 2018 Winter Simulation Conference10.5555/3320516.3320711(1587-1598)Online publication date: 9-Dec-2018
https://dl.acm.org/doi/10.5555/3320516.3320711
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