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Estimating and Validating Nonlinear Regression Metamodels in Simulation
2007
Communications in statistics. Simulation and computation
Frequently, the main objective of statistically designed simulation experiments is to estimate and validate regression metamodels, where the regressors are functions of the design variables and the dependent variable is the system response. In this article, a weighted least squares procedure for estimating the unknown parameters of a nonlinear regression metamodel is formulated and evaluated. Since the validity of a fitted regression model must be tested, a method for validating nonlinear
doi:10.1080/03610910601096288
fatcat:g3thqm6qnbaq5jdwmbpm66cwvq