A Methodology for Fitting and Validating Metamodels in Simulation
AbstractThis expository paper discusses the relationships among metamodels, simulation models, and problem entities. A metamodel or response surface is an approximation of the input/output function implied by the underlying simulation model. There are several types of metamodel: linear regression, splines, neural networks, etc. This paper distinguishes between fitting and validating a metamodel. Metamodels may have different goals: (i) understanding, (ii) prediction, (iii) optimization, and (iv) verification and validation. For this metamodeling, a process with thirteen steps is proposed. Classic design of experiments (DOE) is summarized, including standard measures of fit such as the R-square coefficient and cross-validation measures. This DOE is extended to sequential or stagewise DOE. Several validation criteria, measures, and estimators are discussed. Metamodels in general are covered, along with a procedure for developing linear regression (including polynomial) metamodels.
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Bibliographic InfoPaper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 1997-116.
Date of creation: 1997
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Web page: http://center.uvt.nl
Simulation; approximation; response surface; modelling; regression;
Other versions of this item:
- Kleijnen, Jack P. C. & Sargent, Robert G., 2000. "A methodology for fitting and validating metamodels in simulation," European Journal of Operational Research, Elsevier, vol. 120(1), pages 14-29, January.
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- Kleijnen, J.P.C., 1992.
"Regression metamodels for simulation with common random numbers: Comparison of validation tests and confidence intervals,"
Open Access publications from Tilburg University
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"Verification and validation of simulation models,"
542, Tilburg University, Faculty of Economics and Business Administration.
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