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A methodology for fitting and validating metamodels in simulation. (English) Zbl 0985.65007
Summary: This paper proposes a methodology that replaces the usual ad hoc approach to metamodeling. This methodology considers validation of a metamodel with respect to both the underlying simulation model and the problem entity. It distinguishes between fitting and validating a metamodel, and covers four types of goal: (i) understanding, (ii) prediction, (iii) optimization, and (iv) verification and validation. The methodology consists of a metamodeling process with 10 steps. This process includes classic design of experiments (DOE) and measuring fit through standard measures such as $$R$$-square and cross-validation statistics. The paper extends this DOE to stagewise DOE, and discusses several validation criteria, measures, and estimators. The methodology covers metamodels in general (including neural networks); it also gives a specific procedure for developing linear regression (including polynomial) metamodels for random simulation.

##### MSC:
 65C60 Computational problems in statistics (MSC2010) 62K05 Optimal statistical designs 62J05 Linear regression; mixed models 65D10 Numerical smoothing, curve fitting
bootstrap
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