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ASAP3: a batch means procedure for steady-state simulation analysis

Authors:

Natalie M. Steiger,

James R. Wilson,

Jeffrey A. Joines,

Christos Alexopoulos,

David GoldsmanAuthors Info & Claims

ACM Transactions on Modeling and Computer Simulation (TOMACS), Volume 15, Issue 1

Pages 39 - 73

https://doi.org/10.1145/1044322.1044325

Published: 01 January 2005 Publication History

Abstract

We introduce ASAP3, a refinement of the batch means algorithms ASAP and ASAP2, that delivers point and confidence-interval estimators for the expected response of a steady-state simulation. ASAP3 is a sequential procedure designed to produce a confidence-interval estimator that satisfies user-specified requirements on absolute or relative precision as well as coverage probability. ASAP3 operates as follows: the batch size is progressively increased until the batch means pass the Shapiro-Wilk test for multivariate normality; and then ASAP3 fits a first-order autoregressive (AR(1)) time series model to the batch means. If necessary, the batch size is further increased until the autoregressive parameter in the AR(1) model does not significantly exceed 0.8. Next, ASAP3 computes the terms of an inverse Cornish-Fisher expansion for the classical batch means t-ratio based on the AR(1) parameter estimates; and finally ASAP3 delivers a correlation-adjusted confidence interval based on this expansion. Regarding not only conformance to the precision and coverage-probability requirements but also the mean and variance of the half-length of the delivered confidence interval, ASAP3 compared favorably to other batch means procedures (namely, ABATCH, ASAP, ASAP2, and LBATCH) in an extensive experimental performance evaluation.

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Vandin A(2024)Statistical Model Checking of Python Agent-Based Models: An Integration of MultiVeStA and MesaBridging the Gap Between AI and Reality10.1007/978-3-031-75434-0_26(398-419)Online publication date: 30-Oct-2024
https://dl.acm.org/doi/10.1007/978-3-031-75434-0_26
Lam H(2023)Statistical Uncertainty Quantification for Expensive Black-Box Models: Methodologies and Input Uncertainty ApplicationsProceedings of the Winter Simulation Conference10.5555/3643142.3643266(1501-1515)Online publication date: 10-Dec-2023
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Index Terms

ASAP3: a batch means procedure for steady-state simulation analysis
1. Computing methodologies
  1. Modeling and simulation
    1. Simulation evaluation
2. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Time series analysis

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cover image ACM Transactions on Modeling and Computer Simulation

ACM Transactions on Modeling and Computer Simulation Volume 15, Issue 1

January 2005

107 pages

ISSN:1049-3301

EISSN:1558-1195

DOI:10.1145/1044322

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Copyright © 2005 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 January 2005

Published in TOMACS Volume 15, Issue 1

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Cited By

He SLam H(2024)Higher-order coverage errors of batching methods via Edgeworth expansions on t-statisticsThe Annals of Statistics10.1214/24-AOS237752:4Online publication date: 1-Aug-2024
https://doi.org/10.1214/24-AOS2377
Vandin A(2024)Statistical Model Checking of Python Agent-Based Models: An Integration of MultiVeStA and MesaBridging the Gap Between AI and Reality10.1007/978-3-031-75434-0_26(398-419)Online publication date: 30-Oct-2024
https://dl.acm.org/doi/10.1007/978-3-031-75434-0_26
Lam H(2023)Statistical Uncertainty Quantification for Expensive Black-Box Models: Methodologies and Input Uncertainty ApplicationsProceedings of the Winter Simulation Conference10.5555/3643142.3643266(1501-1515)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3643142.3643266
Lam H(2023)Statistical Uncertainty Quantification for Expensive Black-Box Models: Methodologies and Input Uncertainty Applications2023 Winter Simulation Conference (WSC)10.1109/WSC60868.2023.10407847(1501-1515)Online publication date: 10-Dec-2023
https://doi.org/10.1109/WSC60868.2023.10407847
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Otunuga OLadde GLadde N(2019)Local Lagged Adapted Generalized Method of Moments: An Innovative Estimation and Forecasting Approach and its ApplicationsJournal of Time Series Econometrics10.1515/jtse-2016-002411:1Online publication date: 25-Feb-2019
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Alexopoulos CGoldsman DMokashi ATien KWilson J(2019)SequestOperations Research10.1287/opre.2018.182967:4(1162-1183)Online publication date: 28-Jun-2019
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