Bootstrapping and Validation of Metamodels in Simulation

Bootstrapping is a resampling technique that requires less computer time than simulation does. Bootstrapping -like simulation-must be defined for each type of application. This paper defines bootstrapping for random simulations with replicated runs. The focus is on linear regression metamodels. The metamodel's parameters are estimated through Generalized Least Squares. Its fit is measured through Rao's lack-of-fit F-statistic. The distributions of this statistic is estimated through bootstrapping. The main conclusions are (i) not the regression residuals should be bootstrapped; instead the deviations that also occur in the standard deviation, should be bootstrapped (ii) bootstrapping Rao's lack-of-fit statistic is a good alternative to the F-test: it gives virtually identical results when the assumptions of the F-test are known to apply, and somewhat better results otherwise.

• Type: Conference
• Year: 1998
• Where: WSC
• Authors: Jack P. C. Kleijnen, A. J. Feelders, Russell C. H. Cheng
• Keywords: Generalized Least Squares, Linear Regression Metamodels, Modeling And Simulation, Rao's Lack-of-fit Statistic, WSC 1998