Advanced output analysis for simulation

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Advanced output analysis for simulation

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Winter Simulation Conference archive
Proceedings of the 24th conference on Winter simulation table of contents

Arlington, Virginia, United States

Pages: 190 - 197

Year of Publication: 1992

ISBN:0-7803-0798-4

Author

Andrew F. Seila

Sponsors

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
ORSA : Operations Research Society of America
SIGSIM: ACM Special Interest Group on Simulation and Modeling
TIMS :
IIE : Institute of Industrial Engineers
SCS : Society for Computer Simulation

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 7, Downloads (12 Months): 37, Citation Count: 4

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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	Paul Bratley , Bennett L Fox , Linus E Schrage, A guide to simulation (2nd ed.), Springer-Verlag New York, Inc., New York, NY, 1986
	2	Charnes, J. M. 1989. Statistical analysis of multivariate discrete-event simulation output. Ph.D. Thesis, Department of Operations and Management Science, University of Minnesota, Minneapolis, Minnesota.
	3	John M. Charnes, Power comparisons for the multivariate batch-means method, Proceedings of the 22nd conference on Winter simulation, p.281-287, December 09-12, 1990, New Orleans, Louisiana, United States
	4	John M. Charnes, Multivariate simulation output analysis, Proceedings of the 23rd conference on Winter simulation, p.187-193, December 08-11, 1991, Phoenix, Arizona, United States
	5	Der-Fa R. Chen , Andrew F. Seila, Multivariate inference in stationary simulation using batch means, Proceedings of the 19th conference on Winter simulation, p.302-304, December 14-16, 1987, Atlanta, Georgia, United States [doi>10.1145/318371.318436]
	6	Chow, Y. S. and H. Robbins. 1965. On the asymptotic theory of fixed-width sequential confidence intervals for the mean. Annals of Mathematical Statistics 36: 457-462.
	7	Conway, R. W. 1963. Some tactical problems in digital simulation. Management Science 10: 47- 61.
	8	Michael A. Crane , Donald L. Iglehart, Simulating Stable Stochastic Systems, I: General Multiserver Queues, Journal of the ACM (JACM), v.21 n.1, p.103-113, Jan. 1974 [doi>10.1145/321796.321805]
	9	Crane, M. A. and D. L. Iglehart. 1975. Simulating stable stochastic systems: III. regenerative processes and discrete-event simulations, Operations Research 23: 33-45.
	10	A. A. Crane , J. Lemoine, An Introduction to the Regenerative Method for Simulation Analysis, Springer-Verlag New York, Inc., Secaucus, NJ, 1977
	11	Duket, S. D. and A. A. B. Pritsker. 1978. Examination of simulation output using spectral methods. Math. Comput. Simulation 20: 53-60.
	12	Fishman, G. S. 1972. Bias considerations in simulation experiments. Operations Research 20: 785-790.
	13	Fishman, G. S. 1973. Statistical analysis for queueing simulations. Management Science 20: 363-369.
	14	Fishman, G. S. 1974. Estimation in multiserver queueing simulations. Operations Research 22: 72-78.
	15	George S. Fishman, Achieving specific accuracy in simulation output analysis, Communications of the ACM, v.20 n.5, p.310-315, May 1977 [doi>10.1145/359581.359589]
	16	Fishman, G. S. 1978a. Grouping observations in digital simulation. Management Science 24: 510-521.
	17	George S. Fishman, Principles of Discrete Event Simulation, John Wiley & Sons, Inc., New York, NY, 1978
	18	Fishman, G. S. 1986. Confidence intervals for the mean and a proportion in the bounded case. Technical Report No. UNC/Ol~/TR-86/19, Curriculum in Operations Research and Systems Analysis, University of North Carolina, Chapel Hill, NC.
	19	Gafarian, A. V., C. J. Ancker Jr. and F. Morisaku. 1978. Evaluation of commonly used rules for detecting steady-state" in computer simulation. Naval Research Logistics Quarterly 25: 511-529.
	20	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]
	21	D. Goldsman , M. Meketon , L. Schruben, Properties of standardized time series weighted area variance estimators, Management Science, v.36 n.5, p.602-612, May 1990 [doi>10.1287/mnsc.36.5.602]
	22	Heidelberger, P. and P. A. W. Lewis. 1984. Quantile estimation in dependent sequences. Operations Research 32: 185-209.
	23	Philip Heidelberger , Peter D. Welch, A spectral method for confidence interval generation and run length control in simulations, Communications of the ACM, v.24 n.4, p.233-245, April 1981 [doi>10.1145/358598.358630]
	24	Heidelberger, P. and P. D. Welch. 1981. Adaptive spectral methods for simulation output analysis. IBM Journal of Research and Development 25: 860-876.
	25	Donald L. Iglehart, Simulating Stable Stochastic Systems, VI: Quantile Estimation, Journal of the ACM (JACM), v.23 n.2, p.347-360, April 1976 [doi>10.1145/321941.321954]
	26	Iglehart, D. L. 1978. The regenerative method for simulation analysis. In Current Trends in Programming Methodology, Vol. III. Software Engineemng, K. M. Chandy and R. T. Yeh, Eds. Prentice-Hall, Englewood Cliffs, NJ, 52-71.
	27	Kabak, I. W. 1968. Stopping rules for queuing simulations. Operations Research 16: 431-437.
	28	Kelton, W. D. 1987. Stochastic simulation: initial transient techniques. Systems and Control Encyclopedia; Theory, Technology, Applications, M. G. Singh~ Ed. Pergamon Press, Oxford, 4651- 4654.
	29	Kelton, W. D. and A. M. Law. 1983. A new approach for dealing with the startup problem in discrete event simulation. Naval Research Logistics Quarterly 30:641-658.
	30	Kelton, W. D. 1989. Random initialization methods in simulation. HE Transactions 21" 355-367.
	31	Kleijnen, J. P. C. 1974. Statistzcal techniques in simulation, part I, Marcel Dekker, New York, NY.
	32	Kleijnen, J. P. C. 1975. Statislzcal techniques in simulation, part II, Marcel Dekker, New York, NY.
	33	Kleijnen, J. P. C. 1982. Statistical aspects of simulation: an updated survey.Statistzca Neerlandica 36: 165-186.
	34	Law, A. M. 1977. Confidence intervals in discrete event simulation" a comparison of replication and batch means. Naval Research Logzstics Quarterly 24: 667-678.
	35	Law, A. M. 1980. Statistical analysis of the output data from terminating simulations. Naval Research Logistics Quarterly 27: 131-143.
	36	Law, A. M. 1983. Statistical analysis of simulation output data. Operations Research 31: 983-1029.
	37	Averill M. Law , W. David Kelton, Simulation Modeling and Analysis, McGraw-Hill Higher Education, 1997
	38	Law, A. M. and W. D. Kelton. 1982b. Confidence intervals for steady-state simulations, II: a survey of sequential procedures. Management Science 28: 550-562.
	39	Law, A. M. and W. D. KeIton. 1984. Confidence intervals for steady-state simulations: I. a survey of fixed sample size procedures. Operations Research 32: 1221-1239.
	40	Law, A. M. and J. S. Carson. 1979. A sequential procedure for determining the length of a steadystate simulation. Opcratzons Research 27: 1011- 1025.
	41	P. A. W. Lewis , E. J. Orav, Simulation methodology for statisticians, operations analysts, and engineers: vol. 1, Wadsworth Publ. Co., Belmont, CA, 1989
	42	Mechanic, H. and W. McKay. 1966. Confidence intervals for averages of dependent data in simulations II. Technical Report ASDD 17-202, IBM Corporation, Yorktown Heights, NY.
	43	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
	44	Moore, L. W. 1980. Quantile estimation in regenerative processes. Ph.D. Thesis, Curriculum in Operations Research and Systems Analysis, University of North Carolina, Chapel Hill, NC.
	45	Nadas, A. 1969. An extension of the theorem of Chow and Robbins on sequential confidence intervals for the mean. Annals of Mathematical Statistics 40: 667-671.
	46	Reuven Y. Rubinstein, Simulation and the Monte Carlo Method, John Wiley & Sons, Inc., New York, NY, 1981
	47	Schmeiser, B. W. and K. Kang. 1981. Properties of batch means from stationary ARMA(1,1) time series. Technical Report 81-3, School of Industrial Engineering, Purdue University, Wrest Lafayette, IN.
	48	Schmeiser, B. W. 1982. Batch size effects in the analysis of simulation output. Operations Research 30: 556-568.
	49	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
	50	Schriber, T. J. and R. W. Andrews. 1984. ARMA- based confidence intervals for simulation output analysis. American journal of Mathematical and Management Sciences 4: 345-374.
	51	Lee W. Shruben, Control of initialization bias in multivariate simulation response, Communications of the ACM, v.24 n.4, p.246-252, April 1981 [doi>10.1145/358598.358634]
	52	Schruben, L. W. 1982. Detecting initialization bias in simulation output. Operations Research 30: 569-590.
	53	Schruben, L. W. 1983. Confidence interval estimation using standardized time series. Operations Research 31: 1090-1108.
	54	Schruben, L., I-I. Singh and L. Tierney. 1983. Optimal tests for initialization bias in simulation output, Operations Research 31: 1167-1178.
	55	Seila, A. F. 1982a. A batching approach to quantile estimation in regenerative simulations. Managemenl Science 28: 573-581.
	56	Seila, A. F. 1982b. Percentile estimation in discrete event simulation. Simulation 39: 193- 2OO.
	57	Seila, A. F. 1984. Multivariate simulation output analysis. American Journal of Mathematical and Management Sciences 4: 313-334.
	58	Andrew F. Seila, Output analysis for simulation (tutorial session), Proceedings of the 22nd conference on Winter simulation, p.49-54, December 09-12, 1990, New Orleans, Louisiana, United States
	59	Welch, P. D. 1981. On the problem of the initial transient in steady state simulation. Technical Report, IBM Watson Research Cenl;er, Yorktown Heights, NY.
	60	Welch, P. D. 1983. The statistical analysis of simulation results. The Computer Performance Modeling Handbook, S. S. Lavenberg, Ed., Academic Press, New York, 268-328.
	61	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]
	62	Wilson, J. R. and A. A. B. Pritsker, A. A. B. 1978. A survey of research on the simulation startup problem. Szmulation 31: 55-58.
	63	Wilson, J. R. and A. A. B. Pritsker. 1978. Evaluation of startup policies in simulation experiments. Szmulation 31: 79-89.

CITED BY 4

	W. David Kelton, Analysis of output data, Proceedings of the 26th conference on Winter simulation, p.62-68, December 11-14, 1994, Orlando, Florida, United States
	W. David Kelton, Statistical analysis of simulation output, Proceedings of the 29th conference on Winter simulation, p.23-30, December 07-10, 1997, Atlanta, Georgia, United States
	W. David Kelton, A tutorial on design and analysis of simulation experiments, Proceedings of the 27th conference on Winter simulation, p.24-31, December 03-06, 1995, Arlington, Virginia, United States
	W. David Kelton, Statistical issues in simulation, Proceedings of the 28th conference on Winter simulation, p.47-54, December 08-11, 1996, Coronado, California, United States