Implementing the batch means method in simulation experiments

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Implementing the batch means method in simulation experiments

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Winter Simulation Conference archive
Proceedings of the 28th conference on Winter simulation table of contents

Coronado, California, United States

Pages: 214 - 221

Year of Publication: 1996

ISBN:0-7803-3383-7

Authors

Christos Alexopoulos	School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia
Andrew F. Seila	Terry College of Business, University of Georgia, Athens, GA

Sponsors

INFORMS/CS : Computer Science TC
SIGSIM: ACM Special Interest Group on Simulation and Modeling
IIE : Institute of Industrial Engineers
SCS : Society for Computer Simulation
ASA : American Statistical Association
NIST : National Institue of Standards & Technology
IEEE-CS : Computer Society
IEEE-SMCS : Systems, Man & Cybernetics Society
ACM: Association for Computing Machinery

Publisher

IEEE Computer Society Washington, DC, USA

Bibliometrics

Downloads (6 Weeks): 8, Downloads (12 Months): 25, Citation Count: 1

Additional Information:

abstract references cited by collaborative colleagues

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ABSTRACT

This paper reviews and evaluates strategies for implementing the batch means method for estimating the mean of a stationary simulation output process.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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	2	Carlstein. E. 1986. The use of subseries for estimating the variance of a general statistic from a stationary sequence. Annals of Mathematical Statistics 14:1171-1179.
	3	Chien, C.-H. 1989. Small sample theory for steady state confidence intervals. Technical Report No. 37, Department of Operations Research, Stanford University, Palo Alto, California.
	4	Chiahon Chien , David Goldsman , Benjamin Melamed, Large-sample results for batch means, Management Science, v.43 n.9, p.1288-1295, Sept. 1997 [doi>10.1287/mnsc.43.9.1288]
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	8	George S. Fishman, Principles of Discrete Event Simulation, John Wiley & Sons, Inc., New York, NY, 1978
	9	Fishman, G. S. 1996. Monte Carlo: Concepts, Algorithms and Applications. Chapman and Hall, New York, New York.
	10	Fishman, G. S., and L. S. Yarberry. 1994. An implementation of the batch means method. Technical Report UNC/OR/TR/93-1, Department of Operations Research, University of North Carolina, Chapel Hill, North Carolina.
	11	P. W. Glynn , D. L. Iglehart, Simulation output analysis using standardized time series, Mathematics of Operations Research, v.15 n.1, p.1-16, Feb. 1990 [doi>10.1287/moor.15.1.1]
	12	Heyman, D. P., and M. J. Sobel. 1982. Stochastic Models in Operations Research, Volume i. McGraw-Hill, New York, New York.
	13	Law, A. M., and J. S. Carson. 1979. A sequential procedure for determining the length of a steadystate simulation. Operations Research 27:1011- 1025.
	14	Marc S. Meketon , Bruce Schmeiser, Overlapping batch means: something for nothing?, Proceedings of the 16th conference on Winter simulation, p.226-230, December 28-30, 1984, Dallas, TX
	15	Sidney I. Resnick, Adventures in stochastic processes, Birkhauser Verlag, Basel, Switzerland, 1992
	16	Schmeiser, B. W. 1982. Batch size effects in the analysis of simulation output. Operations Research 30:556-568.
	17	Michael Sherman, On batch means in the simulation and statistics communities, Proceedings of the 27th conference on Winter simulation, p.297-302, December 03-06, 1995, Arlington, Virginia, United States [doi>10.1145/224401.224618]
	18	Wheyming Tina Song , Bruce W. Schmeiser, Optimal mean-squared-error batch sizes, Management Science, v.41 n.1, p.110-123, Jan. 1995 [doi>10.1287/mnsc.41.1.110]
	19	Song, W.-M. T. 1996. On the estimation of optimal batch sizes in the analysis of simulation output. To appear in European Journal of Operations Research.
	20	yon Neumann, J. 1941. Distribution of the ratio of the mean square successive difference and the variance. Annals of Mathematical Statistics 12:367- 395.
	21	Peter D. Welch, On the relationship between batch means, overlapping means and spectral estimation, Proceedings of the 19th conference on Winter simulation, p.320-323, December 14-16, 1987, Atlanta, Georgia, United States [doi>10.1145/318371.318440]

CITED BY

Daniel H. Ockerman , David Goldsman, The impact of transients on simulation variance estimators, Proceedings of the 29th conference on Winter simulation, p.234-239, December 07-10, 1997, Atlanta, Georgia, United States

Collaborative Colleagues:

Christos Alexopoulos: colleagues

Andrew F. Seila: colleagues

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