Kriging Metamodeling in Simulation: A Review
AbstractThis article reviews Kriging (also called spatial correlation modeling). It presents the basic Kriging assumptions and formulas contrasting Kriging and classic linear regression metamodels. Furthermore, it extends Kriging to random simulation, and discusses bootstrapping to estimate the variance of the Kriging predictor. Besides classic one-shot statistical designs such as Latin Hypercube Sampling, it reviews sequentialized and customized designs. It ends with topics for future research.
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Bibliographic InfoPaper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2007-13.
Date of creation: 2007
Date of revision:
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Web page: http://center.uvt.nl
Kriging; Metamodel; Response Surface; Interpolation; Design;
Other versions of this item:
- 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-03-17 (All new papers)
- NEP-CMP-2007-03-17 (Computational Economics)
- NEP-ECM-2007-03-17 (Econometrics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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- repec:hal:wpaper:hal-00759677 is not listed on IDEAS
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- Mehdad, E. & Kleijnen, Jack P.C., 2013. "Bootstrapping and Conditional Simulation in Kriging: Better Confidence Intervals and Optimization," Discussion Paper 2013-038, Tilburg University, Center for Economic Research.
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- Kleijnen, Jack P.C. & Beers, W.C.M. van & Nieuwenhuyse, I. van, 2010. "Constrained optimization in simulation: A novel approach," Open Access publications from Tilburg University urn:nbn:nl:ui:12-3583585, Tilburg University.
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- Didier Rullière & Alaeddine Faleh & Frédéric Planchet & Wassim Youssef, 2013. "Exploring or reducing noise? A global optimization algorithm in the presence of noise," Post-Print hal-00759677, HAL.
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