Computational experience with the batch means method

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Computational experience with the batch means method

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Winter Simulation Conference archive
Proceedings of the 29th conference on Winter simulation table of contents

Atlanta, Georgia, United States

Pages: 194 - 201

Year of Publication: 1997

ISBN:0-7803-4278-X

Authors

Christos Alexopoulos	School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA
George S. Fishman	Department of Operations Research, University of North Carolina, Chapel Hill, NC
Andrew F. Seila	Terry College of Business, University of Georgia, Athens, GA

Sponsors

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

Publisher

IEEE Computer Society Washington, DC, USA

Bibliometrics

Downloads (6 Weeks): 0, Downloads (12 Months): 15, Citation Count: 6

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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.

	1	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.
	2	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.
	3	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]
	4	Conway, R. W. 1963. Some tactical problems in digital simulation. Management Science 10:47-61.
	5	Halim Damerdji, Strong consistency of the variance estimator in steady-state simulation output analysis, Mathematics of Operations Research, v.19 n.2, p.494-512, May 1994 [doi>10.1287/moor.19.2.494]
	6	Fishman, G. S. 1978. Grouping observations in digital simulation. Management Science 24:510-521.
	7	Fishman, G. S. 1996. Monte Carlo: Concepts, algorithms, and applications. New York: Chapman and Hall.
	8	Fishman, G. S., and L. S. Yarberry. 1997. An implementation of the batch means method. To appear in INFORMS Journal on Computing.
	9	Fox, B. L., D. Goldsman, and J. J. Swain. 1990. Spaced batch means. Operations Research Letters 10:255-266.
	10	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]
	11	Law, A. M., and J. S. Carson. 1979. A sequential procedure for determining the length of a steady-state simulation. Operations Research 27:1011-1025.
	12	Mechanic, H., and W. McKay. 1966. Confidence intervals for averages of dependent data in simulations II. Technical Report ASDD 17-202, IBM Corporation, Yorktown Heigths, New York.
	13	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
	14	Sidney I. Resnick, Adventures in stochastic processes, Birkhauser Verlag, Basel, Switzerland, 1992
	15	Schmeiser, B. W. 1982. Batch size effects in the analysis of simulation output. Operations Research 30:556-568.
	16	Thomas J. Schriber , Richard W. Andrews, Interactive analysis of simulation output by the method of batch means, Proceedings of the 11th conference on Winter simulation, p.513-526, December 03-05, 1979, San Diego, CA, United States
	17	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]
	18	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.
	19	von Neumann, J. 1941. Distribution of the ratio of the mean square successive difference and the variance. Annals of Mathematical Statistics 12:367- 395.
	20	Yarberry, L. S. 1993. Incorporating a dynamic batch size selection mechanism in a fixed-samplesize batch means procedure. Ph.D. dissertation, Department of Operations Research, University of North Carolina, Chapel Hill, North Carolina.

CITED BY 6

	George S. Fishman, LABATCH.2: software for statistical analysis of simulation sample path data, Proceedings of the 30th conference on Winter simulation, p.131-140, December 13-16, 1998, Washington, D.C., United States
	David Goldsman , Bruce W. Schmeiser, Computational efficiency of batching methods, Proceedings of the 29th conference on Winter simulation, p.202-207, December 07-10, 1997, Atlanta, Georgia, United States
	Christos Alexopoulos , Andrew F. Seila, Output analysis: output analysis for simulations, Proceedings of the 32nd conference on Winter simulation, December 10-13, 2000, Orlando, Florida
	Christos Alexopoulas , Andrew F. Seila, Advanced methods for simulation output analysi8, Proceedings of the 30th conference on Winter simulation, p.113-120, December 13-16, 1998, Washington, D.C., United States
	Christos Alexopoulos , Andrew F. Seila, Output analysis: output data analysis for simulations, Proceedings of the 33nd conference on Winter simulation, December 09-12, 2001, Arlington, Virginia
	Christos Alexopoulos , Seong-Hee Kim, Statistical analysis of simulation output: output data analysis for simulations, Proceedings of the 34th conference on Winter simulation: exploring new frontiers, December 08-11, 2002, San Diego, California