Analyzing simulation experiments with common random numbers
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.(This abstract was borrowed from another version of this item.)
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Paper provided by Tilburg University in its series Open Access publications from Tilburg University with number urn:nbn:nl:ui:12-369816.Length:
Date of creation: 1988
Date of revision:
Publication status: Published in Management Science (1988) v.34, p.65-74
Handle: RePEc:ner:tilbur:urn:nbn:nl:ui:12-369816
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Web page: http://www.tilburguniversity.edu/
Related research
Keywords:Other versions of this item:
- Jack P. C. Kleijnen, 1988. "Analyzing Simulation Experiments with Common Random Numbers," Management Science, INFORMS, vol. 34(1), pages 65-74, January.
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Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.Cited by:
- 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.
- 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.
- 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 and Governance, Springer, vol. 15(3), pages 447-481, August.
- 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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