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Analyzing Simulation Experiments with Common Random Numbers

Author

Listed:
  • Jack P. C. Kleijnen

    (School of Business and Economics, Catholic University Brabant (Katholieke Universiteit Brabant), 5000 LE Tilburg, Netherlands)

Abstract

To analyze simulation runs which use the same random numbers, the blocking concept of experimental design is not needed. Instead, this paper applies a linear regression model with a nondiagonal covariance matrix. This covariance matrix does not need to have a specific pattern such as constant covariances. A simple example yields surprising results. The paper proposes a new framework for the error analysis. This framework consists of three factors (namely, common random numbers, replication, model validity), each with three levels.

Suggested Citation

  • Jack P. C. Kleijnen, 1988. "Analyzing Simulation Experiments with Common Random Numbers," Management Science, INFORMS, vol. 34(1), pages 65-74, January.
    • Handle: RePEc:inm:ormnsc:v:34:y:1988:i:1:p:65-74
    DOI: 10.1287/mnsc.34.1.65
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    Citations

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    Cited by:

    1. Leutscher, K. J. & Renkema, J. A. & Challa, H., 1999. "Modelling operational adaptations of tactical production plans on pot plant nurseries: a simulation approach," Agricultural Systems, Elsevier, vol. 59(1), pages 67-78, January.
    2. Peter Moran & Michele Simoni & Gianluca Vagnani, 2011. "Becoming the best: by beating or ignoring the best? Toward an expanded view of the role of managerial selection in complex and turbulent environments," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 15(3), pages 447-481, August.
    3. Kleijnen, J.P.C., 1990. "Regression metamodels for simulation with common random numbers : Comparison of techniques," Research Memorandum FEW 426, Tilburg University, School of Economics and Management.
    4. Vagnani, Gianluca, 2009. "The Black-Scholes model as a determinant of the implied volatility smile: A simulation study," Journal of Economic Behavior & Organization, Elsevier, vol. 72(1), pages 103-118, October.
    5. Barry L. Nelson, 2004. "50th Anniversary Article: Stochastic Simulation Research in Management Science," Management Science, INFORMS, vol. 50(7), pages 855-868, July.
    6. Safizadeh, M. Hossein, 2002. "Minimizing the bias and variance of the gradient estimate in RSM simulation studies," European Journal of Operational Research, Elsevier, vol. 136(1), pages 121-135, January.

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