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Simulation output analysis: a wavelet-based spectral method for steady-state simulation analysis

Authors:

Emily K. Lada,

James R. Wilson,

Natalie M. SteigerAuthors Info & Claims

WSC '03: Proceedings of the 35th conference on Winter simulation: driving innovation

Pages 422 - 430

Published: 07 December 2003 Publication History

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Abstract

We develop an automated wavelet-based spectral method for constructing an approximate confidence interval on the steady-state mean of a simulation output process. This procedure, called WASSP, determines a batch size and a warm-up period beyond which the computed batch means form an approximately stationary Gaussian process. Based on the log-smoothed-periodogram of the batch means, WASSP uses wavelets to estimate the batch means log-spectrum and ultimately the steady-state variance constant (SSVC) of the original (unbatched) process. WASSP combines the SSVC estimator with the grand average of the batch means in a sequential procedure for constructing a confidence-interval estimator of the steady-state mean that satisfies user-specified requirements on absolute or relative precision as well as coverage probability. An extensive performance evaluation provides evidence of WASSP's robustness in comparison with some other output analysis methods.

References

[1]

Bruce, A., and H. -Y. Gao. 1996. Applied wavelet analysis with S-PLUS. New York: Springer.

Digital Library

Google Scholar

[2]

Gao, H. -Y. 1997. Choice of thresholds for wavelet shrinkage estimate of the spectrum. Journal of Time Series Analysis 18 (3): 231--251.

Crossref

Google Scholar

[3]

Heidelberger, P., and P. D. Welch. 1981a. A spectral method for confidence interval generation and run length control in simulations. Communications of the ACM 24 (4): 233--245.

Digital Library

Google Scholar

[4]

Heidelberger, P., and P. D. Welch. 1981b. Adaptive spectral methods for simulation output analysis. IBM Journal of Research and Development 25 (6): 860--876.

Digital Library

Google Scholar

[5]

Heidelberger, P., and P. D. Welch. 1983. Simulation run length control in the presence of an initial transient. Operations Research 31 (6): 1109--1144.

Digital Library

Google Scholar

[6]

Lada, E. K. 2003. A wavelet-based procedure for steady-state simulation output analysis. Doctoral dissertation, Graduate Program in Operations Research, North Carolina State University, Raleigh, North Carolina. Available online via <www.lib.ncsu.edu/theses/available/etd-04032003-141616/unrestricted/etd.PDF>.

Google Scholar

[7]

Shapiro, S. S., and M. B. Wilk. 1965. An analysis of variance test for normality (complete samples). Biometrika 52(3--4): 591--611.

Google Scholar

[8]

Steiger, N. M., E. K. Lada, J. R. Wilson, C. Alexopoulos, D. Goldsman, and F. Zouaoui. 2002. ASAP2: An improved batch means procedure for simulation output analysis. In Proceedings of the 2002 Winter Simulation Conference, ed. E. Yücesan, C. -H. Chen, J. L. Snowdon, and J. M. Charnes, 336--344. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers. Available online via <http://www.informs-cs.org/wsc02papers/043.pdf> {accessed December 16, 2002}.

Digital Library

Google Scholar

[9]

Vidakovic, B. 1999. Statistical modeling by wavelets. New York: Wiley.

Google Scholar

[10]

von Neumann, J. 1941. Distribution of the ratio of the mean square successive difference to the variance. Annals of Mathematical Statistics 12:367--395.

Crossref

Google Scholar

Cited By

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Hoad KRobinson SDavies RJefferson TFowler JIngalls R(2008)Automating warm-up length estimationProceedings of the 40th Conference on Winter Simulation10.5555/1516744.1516846(532-540)Online publication date: 7-Dec-2008
https://dl.acm.org/doi/10.5555/1516744.1516846
Lada EWilson JTew J(2007)SBatchProceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come10.5555/1351542.1351635(463-471)Online publication date: 9-Dec-2007
https://dl.acm.org/doi/10.5555/1351542.1351635
Lada EWilson JNicol DFujimoto R(2006)Performance evaluation of spectral procedures for simulation analysisProceedings of the 38th conference on Winter simulation10.5555/1218112.1218154(198-207)Online publication date: 3-Dec-2006
https://dl.acm.org/doi/10.5555/1218112.1218154
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WSC '03: Proceedings of the 35th conference on Winter simulation: driving innovation

December 2003

2094 pages

ISBN:0780381327

General Chair:
David Ferrin
Naperville, IL
,
Program Chair:
Douglas J. Morrice
The University of Texas at Austin, Austin, TX

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Winter Simulation Conference

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Published: 07 December 2003

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WSC03: Winter Simulation Conference 2003

December 7 - 10, 2003

Louisiana, New Orleans

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WSC '03 Paper Acceptance Rate 128 of 189 submissions, 68%;

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

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Hoad KRobinson SDavies RJefferson TFowler JIngalls R(2008)Automating warm-up length estimationProceedings of the 40th Conference on Winter Simulation10.5555/1516744.1516846(532-540)Online publication date: 7-Dec-2008
https://dl.acm.org/doi/10.5555/1516744.1516846
Lada EWilson JTew J(2007)SBatchProceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come10.5555/1351542.1351635(463-471)Online publication date: 9-Dec-2007
https://dl.acm.org/doi/10.5555/1351542.1351635
Lada EWilson JNicol DFujimoto R(2006)Performance evaluation of spectral procedures for simulation analysisProceedings of the 38th conference on Winter simulation10.5555/1218112.1218154(198-207)Online publication date: 3-Dec-2006
https://dl.acm.org/doi/10.5555/1218112.1218154
Steiger NLada EWilson J(2005)Performance evaluation of ASAP3 for steady-state output analysisProceedings of the 37th conference on Winter simulation10.5555/1162708.1162843(781-789)Online publication date: 4-Dec-2005
https://dl.acm.org/doi/10.5555/1162708.1162843
Rossetti MLi ZQu P(2005)Exploring exponentially weighted moving average control charts to determine the warm-up periodProceedings of the 37th conference on Winter simulation10.5555/1162708.1162842(771-780)Online publication date: 4-Dec-2005
https://dl.acm.org/doi/10.5555/1162708.1162842
Chen EKelton W(2004)Experimental performance evaluation of histogram approximation for simulation output analysisProceedings of the 36th conference on Winter simulation10.5555/1161734.1161859(685-693)Online publication date: 5-Dec-2004
https://dl.acm.org/doi/10.5555/1161734.1161859
Steiger NLada EWilson JJoines JAlexopoulos CGoldsman D(2004)Steady-state simulation analysis using ASAP3Proceedings of the 36th conference on Winter simulation10.5555/1161734.1161856(672-680)Online publication date: 5-Dec-2004
https://dl.acm.org/doi/10.5555/1161734.1161856

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Mean-Square Consistency of the Variance Estimator in Steady-State Simulation Output Analysis

Output analysis of multiclass fluid models with static priorities

Jackknifed variance estimators for simulation output analysis

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