Optimal Model Structure Identification 2 Nonlinear Regression (original) (raw)

Nonlinear regression consists in finding the best possible model parameter values of a given homoscedastic mathematical structure with nonlinear functions of the model parameters. In this report, the second part of the series, the mathematical structure of models with nonlinear functions of their parameters is optimized, resulting in the minimum estimation of model error variance. The uncertainty in the estimation of model parameters is evaluated using a linear approximation of the model about the optimal model parameter values found. The homoscedasticity of model residuals must be evaluated to validate this important assumption. The model structure identification procedure is implemented in R language and shown in the Appendix. Several examples are considered for illustrating the optimization procedure. In many practical situations, the optimal model obtained has heteroscedastic residuals. If the purpose of the model is only describing the experimental observations, the violation of the homoscedastic assumption may not be critical. However, for explanatory or extrapolating models, the presence of heteroscedastic residuals may lead to flawed conclusions.