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. The 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 C.R. Rao´s (1959) 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 because it gives virtually identical results when the assumptions of the F-test are known to apply, and somewhat better results otherwise