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Validation of Simulation Analysis Methods for the Schruben-Margolin Correlation-Induction Strategy

Author

Listed:
  • Jeffrey D. Tew

    (Virginia Polytechnic Institute and State University, Blacksburg, Virginia)

  • James R. Wilson

    (North Carolina State University, Raleigh, North Carolina)

Abstract

To analyze simulation experiments performed under the Schruben-Margolin strategy for assigning random number streams to individual runs, A. Nozari, S. Arnold, and C. Pegden developed special statistical methods for estimating a general linear metamodel (that is, a regression model) of a selected response variable expressed in terms of design variables (regressors) relevant to the target system. This paper describes a three-stage procedure for validating the use of these simulation analysis methods. Each stage of the validation procedure tests a key assumption about the behavior of the response across all points in the experimental design. The first stage checks for multivariate normality in the overall set of responses, the second stage checks for the Schruben-Margolin covariance structure among those responses, and the third stage checks for adequacy (goodness of fit) of the user-specified metamodel. To handle simulation experiments that display significant departures from the Schruben-Margolin covariance structure, we present alternative versions of the goodness-of-fit test and the follow-up analysis for the postulated metamodel that merely requires jointly normal responses. A numerical example illustrates the application of this validation procedure.

Suggested Citation

  • Jeffrey D. Tew & James R. Wilson, 1992. "Validation of Simulation Analysis Methods for the Schruben-Margolin Correlation-Induction Strategy," Operations Research, INFORMS, vol. 40(1), pages 87-103, February.
    • Handle: RePEc:inm:oropre:v:40:y:1992:i:1:p:87-103
    DOI: 10.1287/opre.40.1.87
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    Citations

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    Cited by:

    1. T. Glenn Bailey & Paul A. Jensen & David P. Morton, 1999. "Response surface analysis of two‐stage stochastic linear programming with recourse," Naval Research Logistics (NRL), John Wiley & Sons, vol. 46(7), pages 753-776, October.
    2. Jack P. C. Kleijnen & Bert Bettonvil & Willem Van Groenendaal, 1998. "Validation of Trace-Driven Simulation Models: A Novel Regression Test," Management Science, INFORMS, vol. 44(6), pages 812-819, June.
    3. Bruce Ankenman & Barry L. Nelson & Jeremy Staum, 2010. "Stochastic Kriging for Simulation Metamodeling," Operations Research, INFORMS, vol. 58(2), pages 371-382, April.
    4. Jing Xie & Peter I. Frazier & Stephen E. Chick, 2016. "Bayesian Optimization via Simulation with Pairwise Sampling and Correlated Prior Beliefs," Operations Research, INFORMS, vol. 64(2), pages 542-559, April.
    5. Natalie M. Steiger & James R. Wilson, 2002. "An Improved Batch Means Procedure for Simulation Output Analysis," Management Science, INFORMS, vol. 48(12), pages 1569-1586, December.
    6. Joshi, Shirish & Tew, Jeffrey D., 1995. "Validation and statistical analysis procedures under the common random number correlation-induction strategy for multipopulation simulation experiments," European Journal of Operational Research, Elsevier, vol. 85(1), pages 205-220, August.

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