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...

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Bibliographic Details
Published in:European journal of operational research 2009-02, Vol.192 (3), p.707-716
Main Author: Kleijnen, Jack P.C.
Format: Article
Language:English
Subjects:
Bootstrap method
Design
Exact sciences and technology
Interpolation
Kriging
Kriging Metamodel Response surface Interpolation Optimization Design
Linear inference, regression
Mathematical models
Mathematics
Metamodel
Optimization
Probability and statistics
Regression analysis
Response surface
Sciences and techniques of general use
Simulation
Statistics
Studies
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Summary: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.
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2007.10.013