Model selection among growth curve models that have the same number of parameters

Abstract

Abstract A model selection method was proposed to determine the most appropriate model among growth curve models that have the same number of parameters. It uses a measure of mean relative squared error and regression equations from difference equations for growth curve models. The difference equations have exact solutions that are on exact solutions of differential equations as growth curve models. The regression equations from the difference equations perfectly reproduce their parameter estimates. The proposed method selects an appropriate model when data are on an exact solution of a differential equation. It was verified to be practical with six actual datasets when the Gompertz curve and logistic curve models, which are often used for forecasting, were alternative growth curve models.