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  • Bootstrap_CIs_for_simulation_output_parameter_WSC2023

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Bootstrap Confidence Intervals for Simulation Output Parameters

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Bootstrap Confidence Intervals for Simulation Output Parameters. / Barton, Russell; Rhodes-Leader, Luke.
Proceedings of the 2023 Winter Simulation Conference. New York: ACM, 2024. p. 421-432.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Barton, R & Rhodes-Leader, L 2024, Bootstrap Confidence Intervals for Simulation Output Parameters. in Proceedings of the 2023 Winter Simulation Conference. ACM, New York, pp. 421-432. https://doi.org/10.1109/WSC60868.2023.10407467

APA

Barton, R., & Rhodes-Leader, L. (2024). Bootstrap Confidence Intervals for Simulation Output Parameters. In Proceedings of the 2023 Winter Simulation Conference (pp. 421-432). ACM. https://doi.org/10.1109/WSC60868.2023.10407467

Vancouver

Barton R, Rhodes-Leader L. Bootstrap Confidence Intervals for Simulation Output Parameters. In Proceedings of the 2023 Winter Simulation Conference. New York: ACM. 2024. p. 421-432 Epub 2023 Dec 10. doi: 10.1109/WSC60868.2023.10407467

Author

Barton, Russell ; Rhodes-Leader, Luke. / Bootstrap Confidence Intervals for Simulation Output Parameters. Proceedings of the 2023 Winter Simulation Conference. New York : ACM, 2024. pp. 421-432

Bibtex

@inproceedings{0e46b87a0b154f75a1c37fcd42722043,
title = "Bootstrap Confidence Intervals for Simulation Output Parameters",
abstract = "Bootstrapping has been used to characterize the impact on discrete-event simulation output arising from input model uncertainty for thirty years. The distribution of simulation output statistics can be very non-normal, especially in simulation of heavily loaded queueing systems, and systems operating at a near optimal value of the output measure. This paper presents issues facing simulationists in using bootstrapping to provide confidence intervals for parameters related to the distribution of simulation output statistics, and identifies appropriate alternatives to the basic and percentile bootstrap methods. Both input uncertainty and ordinary output analysis settings are included.",
author = "Russell Barton and Luke Rhodes-Leader",
year = "2024",
month = feb,
day = "2",
doi = "10.1109/WSC60868.2023.10407467",
language = "English",
pages = "421--432",
booktitle = "Proceedings of the 2023 Winter Simulation Conference",
publisher = "ACM",

}

RIS

TY - GEN

T1 - Bootstrap Confidence Intervals for Simulation Output Parameters

AU - Barton, Russell

AU - Rhodes-Leader, Luke

PY - 2024/2/2

Y1 - 2024/2/2

N2 - Bootstrapping has been used to characterize the impact on discrete-event simulation output arising from input model uncertainty for thirty years. The distribution of simulation output statistics can be very non-normal, especially in simulation of heavily loaded queueing systems, and systems operating at a near optimal value of the output measure. This paper presents issues facing simulationists in using bootstrapping to provide confidence intervals for parameters related to the distribution of simulation output statistics, and identifies appropriate alternatives to the basic and percentile bootstrap methods. Both input uncertainty and ordinary output analysis settings are included.

AB - Bootstrapping has been used to characterize the impact on discrete-event simulation output arising from input model uncertainty for thirty years. The distribution of simulation output statistics can be very non-normal, especially in simulation of heavily loaded queueing systems, and systems operating at a near optimal value of the output measure. This paper presents issues facing simulationists in using bootstrapping to provide confidence intervals for parameters related to the distribution of simulation output statistics, and identifies appropriate alternatives to the basic and percentile bootstrap methods. Both input uncertainty and ordinary output analysis settings are included.

U2 - 10.1109/WSC60868.2023.10407467

DO - 10.1109/WSC60868.2023.10407467

M3 - Conference contribution/Paper

SP - 421

EP - 432

BT - Proceedings of the 2023 Winter Simulation Conference

PB - ACM

CY - New York

ER -