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A methodology for fitting and validating metamodels in simulation

  • Kleijnen, Jack P. C.
  • Sargent, Robert G.

This 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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File URL: http://www.sciencedirect.com/science/article/B6VCT-3XMPN89-2/2/9c52750398bfbcf28fdd6db2d0240b5b
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Article provided by Elsevier in its journal European Journal of Operational Research.

Volume (Year): 120 (2000)
Issue (Month): 1 (January)
Pages: 14-29

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Handle: RePEc:eee:ejores:v:120:y:2000:i:1:p:14-29
Contact details of provider: Web page: http://www.elsevier.com/locate/eor

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  1. Jack P. C. Kleijnen, 1992. "Regression Metamodels for Simulation with Common Random Numbers: Comparison of Validation Tests and Confidence Intervals," Management Science, INFORMS, vol. 38(8), pages 1164-1185, August.
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