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Stochastic Intrinsic Kriging for Simulation Metamodelling

Author

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  • Mehdad, Ehsan

    (Tilburg University, Center For Economic Research)

  • Kleijnen, J.P.C.

    (Tilburg University, Center For Economic Research)

Abstract

We derive intrinsic Kriging, using Matherons intrinsic random functions which eliminate the trend in classic Kriging. We formulate this intrinsic Kriging as a metamodel in deterministic and random simulation models. For random simulation we derive an experimental design that also specifies the number of replications that varies with the input combinations. We compare intrinsic Kriging and classic Kriging in several numerical experiments with deterministic and random simulations. These experiments suggest that intrinsic Kriging gives more accurate metamodel, in most experiments.
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Suggested Citation

  • Mehdad, Ehsan & Kleijnen, J.P.C., 2015. "Stochastic Intrinsic Kriging for Simulation Metamodelling," Discussion Paper 2015-038, Tilburg University, Center for Economic Research.
    • Handle: RePEc:tiu:tiucen:00bed9cb-d34c-4e98-93ef-e805fce63fa0
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    References listed on IDEAS

    as
    1. Bruce Ankenman & Barry L. Nelson & Jeremy Staum, 2010. "Stochastic Kriging for Simulation Metamodeling," Operations Research, INFORMS, vol. 58(2), pages 371-382, April.
    2. E. Vazquez & E. Walter & G. Fleury, 2005. "Intrinsic Kriging and prior information," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 21(2), pages 215-226, March.
    3. Ward Whitt, 1989. "Planning Queueing Simulations," Management Science, INFORMS, vol. 35(11), pages 1341-1366, November.
    4. Hong Wan & Bruce E. Ankenman & Barry L. Nelson, 2010. "Improving the Efficiency and Efficacy of Controlled Sequential Bifurcation for Simulation Factor Screening," INFORMS Journal on Computing, INFORMS, vol. 22(3), pages 482-492, August.
    5. J. D. Opsomer & D. Ruppert & M. P. Wand & U. Holst & O. Hössjer, 1999. "Kriging with Nonparametric Variance Function Estimation," Biometrics, The International Biometric Society, vol. 55(3), pages 704-710, September.
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    Cited by:

    1. Ehsan Mehdad & Jack P. C. Kleijnen, 2018. "Efficient global optimisation for black-box simulation via sequential intrinsic Kriging," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(11), pages 1725-1737, November.
    2. Mehdad, E. & Kleijnen, Jack P.C., 2014. "Global Optimization for Black-box Simulation via Sequential Intrinsic Kriging," Discussion Paper 2014-063, Tilburg University, Center for Economic Research.
    3. Mehdad, E., 2015. "Kriging metamodels and global opimization in simulation," Other publications TiSEM 5b5c276a-fe68-4ce9-b8a8-1, Tilburg University, School of Economics and Management.
    4. Nathan E. Wilson, 2021. "The Impact of Competition on Investment: Evidence From California Hospitals," Journal of Industrial Economics, Wiley Blackwell, vol. 69(1), pages 1-32, March.
    5. Feng, Ben Mingbin & Li, Johnny Siu-Hang & Zhou, Kenneth Q., 2022. "Green nested simulation via likelihood ratio: Applications to longevity risk management," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 285-301.

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    More about this item

    Keywords

    simulation; gaussian process; Kriging; intrinsic random functions; metamodel;
    All these keywords.

    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory

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