Output analysis: analysis of simulation output

ABSTRACT

We discuss methods for statistically analyzing the output from stochastic discrete-event or Monte Carlo simulations. Both terminating and steady-state simulations are considered.

REFERENCES

• 1: Anderson, T. W. 1994. The Statistical Analysis of Time Series, New York: Wiley.
• 2: Andradóttir, S. 1998. Simulation optimization. Chapter 9 in Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice. Ed. J. Banks. New York: John Wiley and Sons.
• 3: Banks, J. 1998. Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice. New York: John Wiley and Sons.
• 4: Banks, J., J. S. Carson, II, B. L. Nelson, and D. M. Nicol. 2001. Discrete-Event System Simulation, 3rd edition. Upper Saddle River, New Jersey: Prentice-Hall, Inc.
• 5: Paul Bratley , Bennett L. Fox , Linus E. Schrage, A guide to simulation (2nd ed.), Springer-Verlag New York, Inc., New York, NY, 1987
• 6: Crane, M. and D. L. Iglehart. 1975. Simulating stable stochastic systems, III: Regenerative processes and discrete-event simulations. Operations Research 23:33--45.
• 7: George S. Fishman, Discrete-event simulation, Springer-Verlag, London, 2001
• 8: Fu, M. and J. Q. Hu. 1997. Conditional Monte Carlo: Gradient Estimation and Optimization Applications. Boston: Kluwer Academic Publishers.
• 9: Glasserman, P. 1991. Gradient Estimation via Perturbation Analysis. Boston: Kluwer Academic Publishers.
• 10: Peter W. Glynn, Likelihood ratio gradient estimation for stochastic systems, Communications of the ACM, v.33 n.10, p.75-84, Oct. 1990 [doi>10.1145/84537.84552]
• 11: P W. Glynn , D. L. Iglehart, Importance sampling for stochastic simulations, Management Science, v.35 n.11, p.1367-1392, Nov. 1989 [doi>10.1287/mnsc.35.11.1367]
• 12: David Goldsman , Barry L. Nelson, Statistical selection of the best system, Proceedings of the 33nd conference on Winter simulation, December 09-12, 2001, Arlington, Virginia
• 13: Hammersley, J. M. and D. C. Handscomb. 1964. Monte Carlo Methods. London: Methuen.
• 14: Philip Heidelberger, Fast simulation of rare events in queueing and reliability models, ACM Transactions on Modeling and Computer Simulation (TOMACS), v.5 n.1, p.43-85, Jan. 1995 [doi>10.1145/203091.203094]
• 15: Ho, Y. C. and X. R. Cao. 1991. Discrete Event Dynamic Systems and Perturbation Analysis. Boston: Kluwer Academic Publishers.
• 16: Averill M. Law , David M. Kelton, Simulation Modeling and Analysis, McGraw-Hill Higher Education, 1999
• 17: Melamed, B. and Rubinstein, R. Y. 1998. Modern Simulation and Modeling. New York: John Wiley and Sons, Inc.
• 18: Marvin K. Nakayama, Two-stage stopping procedures based on standardized time series, Management Science, v.40 n.9, p.1189-1206, Sept. 1994 [doi>10.1287/mnsc.40.9.1189]
• 19: Nicola, V. F., P. Shahabuddin and M. K. Nakayama. 2001. Techniques for fast simulation of models of highly dependable systems. IEEE Trans. Reliability 50: 246--264.
• 20: Reiman, M. I. and A. Weiss. 1989. Sensitivity analysis for simulations via likelihood ratios. Operations Research 37: 830--844.
• 21: Sheldon M. Ross, Simulation, Academic Press, Inc., Orlando, FL, 2002
• 22: R. Y. Rubinstein, Sensitivity analysis and performance extrapolation for computer simulation models, Operations Research, v.37 n.1, p.72-81, Jan.-Feb. 1989 [doi>10.1287/opre.37.1.72]
• 23: Schmeiser, B. W. 1982. Batch size effects in the analysis of simulation output. Operations Research 30: 556--568.
• 24: Bruce W. Schmeiser , Wheyming Tina Song, Batching methods in simulation output analysis: what we know and what we don't, Proceedings of the 28th conference on Winter simulation, p.122-127, December 08-11, 1996, Coronado, California, United States [doi>10.1145/256562.256589]
• 25: Schruben, L. W. 1983. Confidence interval estimation using standardized time series. Operations Research 31: 1090--1108.
• 26: Shedler, G. S. 1993. Regenerative Stochastic Simulation. San Diego: Academic Press.
• 27: Stein, C. 1945. A two-sample test for a linear hypothesis whose power is independent of the variance. Annals of Mathematical Statistics 16: 243--258.
• 28: Ward Whitt, The efficiency of one long run versus independent replication in steady-state simulation, Management Science, v.37 n.6, p.645-666, June 1991 [doi>10.1287/mnsc.37.6.645]