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Issues in the optimal design of computer simulation experiments


  • Werner Müller
  • Milan Stehlík


Output from computer simulation experiments is often approximated as realizations of correlated random fields. Consequently, the corresponding optimal design questions must cope with the existence and detection of an error correlation structure, issues largely unaccounted for by traditional optimal design theory. Unfortunately, many of the nice features of well‐established design techniques, such as additivity of the information matrix, convexity of design criteria, etc., do not carry over to the setting of interest. This may lead to unexpected, counterintuitive, even paradoxical effects in the design as well as the analysis stage of computer simulation experiments. In this paper we intend to give an overview and some simple but illuminating examples of this behaviour. Copyright © 2009 John Wiley & Sons, Ltd.

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  • Werner Müller & Milan Stehlík, 2009. "Issues in the optimal design of computer simulation experiments," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(2), pages 163-177, March.
  • Handle: RePEc:wly:apsmbi:v:25:y:2009:i:2:p:163-177
    DOI: 10.1002/asmb.740

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    References listed on IDEAS

    1. Hao Zhang & Dale L. Zimmerman, 2005. "Towards reconciling two asymptotic frameworks in spatial statistics," Biometrika, Biometrika Trust, vol. 92(4), pages 921-936, December.
    2. Dale Zimmerman & Noel Cressie, 1992. "Mean squared prediction error in the spatial linear model with estimated covariance parameters," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 44(1), pages 27-43, March.
    3. Goos, Peter & Kobilinsky, Andre & O'Brien, Timothy E. & Vandebroek, Martina, 2005. "Model-robust and model-sensitive designs," Computational Statistics & Data Analysis, Elsevier, vol. 49(1), pages 201-216, April.
    4. Zhang, Hao, 2004. "Inconsistent Estimation and Asymptotically Equal Interpolations in Model-Based Geostatistics," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 250-261, January.
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