Kriging metamodeling in simulation: A review
This 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 for sensitivity analysis and optimization. It ends with topics for future research.
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- den Hertog, D. & Kleijnen, J.P.C. & Siem, A.Y.D., 2004. "The Correct Kriging Variance Estimated by Bootstrapping," Discussion Paper 2004-46, Tilburg University, Center for Economic Research.
- Kleijnen, Jack P. C. & van Beers, Wim C. M., 2005. "Robustness of Kriging when interpolating in random simulation with heterogeneous variances: Some experiments," European Journal of Operational Research, Elsevier, vol. 165(3), pages 826-834, September.
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