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. It ends with topics for future research.
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Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number
2007-13.
Find related papers by JEL classification: C0 - Mathematical and Quantitative Methods - - General C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General C9 - Mathematical and Quantitative Methods - - Design of Experiments
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