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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Vydáno v:European journal of operational research Ročník 192; číslo 3; s. 707 - 716
Hlavní autor: Kleijnen, Jack P.C.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 01.02.2009
Elsevier
Elsevier Sequoia S.A
Edice:European Journal of Operational Research
Témata:
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
Design
Interpolation
Response surface
Metamodel
Kriging
Optimization
Sensitivity analysis
Statistical analysis
Probabilistic approach
Linear regression
Spatial correlation
Customization
Regression analysis
Modeling
Variance
Metaanalysis
Bootstrapping
Latin hypercube sampling
Bootstrap
ISSN:0377-2217, 1872-6860
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Popis
Shrnutí: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.
Bibliografie:SourceType-Scholarly Journals-1
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ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2007.10.013