skip to main content
10.5555/510378.510398acmconferencesArticle/Chapter ViewAbstractPublication PagesConference Proceedingsacm-pubtype
Article
Free access

Output analysis: output analysis for simulations

Published: 10 December 2000 Publication History

Abstract

This paper reviews statistical methods for analyzing output data from computer simulations of single systems. In particular, it focuses on the estimation of steady-state system parameters. The estimation techniques include the replication/deletion approach, the regenerative method, the batch means method, and the standardized time series method.

References

[1]
Alexopoulos, C., and A. F. Seila. 1998. Output data analysis. In Handbook of Simulation, ed. J. Banks, Chapter 7, New York: John Wiley & Sons.
[2]
Alexopoulos, C., A. F. Seila and G. S. Fishman. 1998. Computational experience with the batch means method. In Proceedings of the 1997 Winter Simulation Conference, 194-201. Institute of Electrical and Electronics Engineers, Piscataway, New Jersey.
[3]
Billingsley, P. 1968. Convergence of probability measures, Wiley, New York.
[4]
Bratley, P., B. L. Fox, and L. E. Schrage. 1987. A guide to simulation, 2d Ed. Springer-Verlag, New York, New York.
[5]
Chance, F., and L. W. Schruben. 1992. Establishing a truncation point in simulation output. Technical Report, School of Operations Research and Industrial Engineering, Cornell University, Ithaca, New York.
[6]
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.
[7]
Charnes, J. M. 1990. Power comparisons for the multivariate batch-means method. In Proceedings of the 1990 Winter Simulation Conference, 281-287. IEEE, Piscataway, New Jersey.
[8]
Charnes, J. M. 1991. Multivariate simulation output analysis. In Proceedings of the 1991 Winter Simulation Conference, 187-193. IEEE, Piscataway, New Jersey.
[9]
Chen, R. D., and A. F. Seila. 1987. Multivariate inference in stationary simulation using batch means. In Proceedings of the 1987 Winter Simulation Conference, 302-304. IEEE, Piscataway, New Jersey.
[10]
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.
[11]
Chien, C., D. Goldsman, and B. Melamed. 1997. Large-sample results for batch means. Management Science 43:1288-1295.
[12]
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.
[13]
Conway, R. W. 1963. Some tactical problems in digital simulation. Management Science 10:47-61.
[14]
Crane, M. A., and D. L. Iglehart. 1974a. Simulating stable stochastic systems, I: General multiserver queues. Journal of the ACM 21:103-113.
[15]
Crane, M. A., and D. L. Iglehart. 1974b. Simulating stable stochastic systems, II: Markov chains. Journal of the ACM 21:114-123.
[16]
Crane, M. A., and D. L. Iglehart. 1975. Simulating stable stochastic systems III: Regenerative processes and discrete-event simulations. Operations Research 23:33-45.
[17]
Damerdji, H. 1994. Strong consistency of the variance estimator in steady-state simulation output analysis. Mathematics of Operations Research 19:494-512.
[18]
Fishman, G. S. 1973. Statistical analysis for queueing simulations. Management Science 20:363-369.
[19]
Fishman, G. S. 1974. Estimation of multiserver queueing simulations. Operations Research 22:72-78.
[20]
Fishman, G. S. 1978. Principles of discrete event simulation, New York: John Wiley & Sons.
[21]
Fishman, G. S. 1996. Monte Carlo: Concepts, algorithms, and applications. New York: Springer Verlag.
[22]
Fishman, G. S. 2000. Private communication.
[23]
Fishman, G. S., and L. S. Yarberry. 1997. An implementation of the batch means method. INFORMS Journal on Computing 9:296-310.
[24]
Gafarian, A. V., C. J. Ancker, and F. Morisaku. 1978. Evaluation of commonly used rules for detecting steady-state in computer simulation. Naval Research Logistics Quarterly 25:511-529.
[25]
Glynn, P. W., and D. L. Iglehart. 1990. Simulation analysis using standardized time series. Mathematics of Operations Research 15:1-16.
[26]
Goldsman, D., M. Meketon, and L. W. Schruben. 1990. Properties of standardized time series weighted area variance estimators. Management Science 36:602-612.
[27]
Goldsman, D., and L. W. Schruben. 1984. Asymptotic properties of some confidence interval estimators for simulation output. Management Science 30:1217-1225.
[28]
Goldsman, D., and L. W. Schruben. 1990. New confidence interval estimators using standardized time series. Management Science 36:393-397.
[29]
Goldsman, D., L. W. Schruben, and J. J. Swain. 1994. Tests for transient means in simulated time series. Naval Research Logistics 41:171-187.
[30]
Heidelberger, P., and P. A. W. Lewis. 1984. Quantile estimation in dependent sequences. Operations Research 32:185-209.
[31]
Heidelberger, P., and P. D. Welch. 1981a. A spectral method for confidence interval generation and run length control in simulations. Communications of the ACM 24:233-245.
[32]
Iglehart, D. L. 1975. Simulating stable stochastic systems, V: Comparison of ratio estimators. Naval Research Logistics Quarterly 22:553-565.
[33]
Iglehart, D. L. 1976. Simulating stable stochastic systems, VI: Quantile estimation. Journal of the ACM 23:347-360.
[34]
Iglehart, D. L. 1978. The regenerative method for simulation analysis. In Current Trends in Programming Methodology, Vol. III, eds. K. M. Chandy, and K. M. Yeh, 52-71. Prentice-Hall, Englewood Cliffs, New Jersey.
[35]
Kelton, W. D. 1989. Random initialization methods in simulation. IIE Transactions 21:355-367.
[36]
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.
[37]
Law, A. M., and W. D. Kelton. 2000. Simulation modeling and analysis, 3d Ed. McGraw-Hill, New York.
[38]
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, New York.
[39]
Meketon, M. S., and B. W. Schmeiser. 1984. Overlapping batch means: Something for nothing? In Proceedings of the 1984 Winter Simulation Conference, 227-230. IEEE, Piscataway, New Jersey.
[40]
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, North Carolina.
[41]
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.
[42]
Ockerman, D. H. 1995. Initialization bias tests for stationary stochastic processes based upon standardized time series techniques. Ph.D. Thesis, School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia.
[43]
Resnick, S. I. 1994. Adventures in stochastic processes. Boston: Birkhaüser.
[44]
Sargent, R. G., K. Kang, and D. Goldsman. 1992. An investigation of finite-sample behavior of confidence interval estimators. Operations Research 40:898-913.
[45]
Schmeiser, B. W. 1982. Batch size effects in the analysis of simulation output. Operations Research 30:556-568.
[46]
Schriber, T. J., and R. W. Andrews. 1979. Interactive analysis of simulation output by the method of batch means. In Proceedings of the 1979 Winter Simulation Conference, 513-525. IEEE, Piscataway, New Jersey.
[47]
Schruben, L. W. 1982. Detecting initialization bias in simulation output. Operations Research 30:569-590.
[48]
Schruben, L. W. 1983. Confidence interval estimation using standardized time series. Operations Research 31:1090-1108.
[49]
Schruben, L. W., H. Singh, and L. Tierney. 1983. Optimal tests for initialization bias in simulation output. Operations Research 31:1167-1178.
[50]
Seila, A. F. 1982a. A batching approach to quantile estimation in regenerative simulations. Management Science 28:573-581.
[51]
Seila, A. F. 1982b. Percentile estimation in discrete event simulation. Simulation 39:193-200.
[52]
Song, W.-M. T., and B. W. Schmeiser. 1993. Optimal mean-squared-error batch sizes. Management Science 41:110-123.
[53]
von Neumann, J. 1941a. The mean square difference. Annals of Mathematical Statistics 12:153-162.
[54]
von Neumann, J. 1941b. Distribution of the ratio of the mean square successive difference and the variance. Annals of Mathematical Statistics 12:367-395.
[55]
Welch, P. D. 1983. The statistical analysis of simulation results. In The computer performance modeling handbook, ed. S. Lavenberg, 268-328. New York: Academic Press.
[56]
Welch, P. D. 1987. On the relationship between batch means, overlapping batch means and spectral estimation, In Proceedings of the 1987 Winter Simulation Conference, 320-323. IEEE, Piscataway, New Jersey.
[57]
Wilson, J. R., and A. A. B. Pritsker. 1978a. A survey of research on the simulation startup problem. Simulation 31:55-58.
[58]
Wilson, J. R., and A. A. B. Pritsker. 1978b. Evaluation of startup policies in simulation experiments. Simulation 31:79-89.
[59]
Yarberry, L. S. 1993. Incorporating a dynamic batch size selection mechanism in a fixed-sample-size batch means procedure. Ph.D. dissertation, Department of Operations Research, University of North Carolina, Chapel Hill, North Carolina.
[60]
Young, L. C. 1941. On randomness of order sequences. Annals of Mathematical Statistics 12:293-300.

Cited By

View all
  • (2004)Statistical analysis of simulation output dataProceedings of the 36th conference on Winter simulation10.5555/1161734.1161752(67-72)Online publication date: 5-Dec-2004
  • (2001)Quantifying simulation output variability using confidence intervals and statistical process controlProceedings of the 33nd conference on Winter simulation10.5555/564124.564251(896-901)Online publication date: 9-Dec-2001
  • (2000)Output analysisProceedings of the 32nd conference on Winter simulation10.5555/510378.510388(39-45)Online publication date: 10-Dec-2000

Index Terms

  1. Output analysis: output analysis for simulations
    Index terms have been assigned to the content through auto-classification.

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    WSC '00: Proceedings of the 32nd conference on Winter simulation
    December 2000
    2014 pages

    Sponsors

    • IIE: Institute of Industrial Engineers
    • ASA: American Statistical Association
    • SIGSIM: ACM Special Interest Group on Simulation and Modeling
    • IEEE/CS: Institute of Electrical and Electronics Engineers/Computer Society
    • NIST: National Institute of Standards and Technology
    • INFORMS-CS: Institute for Operations Research and the Management Sciences-College on Simulation
    • IEEE/SMCS: Institute of Electrical and Electronics Engineers/Systems, Man, and Cybernetics Society
    • SCS: The Society for Computer Simulation International

    Publisher

    Society for Computer Simulation International

    San Diego, CA, United States

    Publication History

    Published: 10 December 2000

    Check for updates

    Qualifiers

    • Article

    Conference

    WSC00
    Sponsor:
    • IIE
    • ASA
    • SIGSIM
    • IEEE/CS
    • NIST
    • INFORMS-CS
    • IEEE/SMCS
    • SCS
    WSC00: Winter Simulation Conference
    December 10 - 13, 2000
    Florida, Orlando

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)7
    • Downloads (Last 6 weeks)5
    Reflects downloads up to 15 Sep 2024

    Other Metrics

    Citations

    Cited By

    View all
    • (2004)Statistical analysis of simulation output dataProceedings of the 36th conference on Winter simulation10.5555/1161734.1161752(67-72)Online publication date: 5-Dec-2004
    • (2001)Quantifying simulation output variability using confidence intervals and statistical process controlProceedings of the 33nd conference on Winter simulation10.5555/564124.564251(896-901)Online publication date: 9-Dec-2001
    • (2000)Output analysisProceedings of the 32nd conference on Winter simulation10.5555/510378.510388(39-45)Online publication date: 10-Dec-2000

    View Options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Get Access

    Login options

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media