Optimal Design for Models Incorporating the Richards Function

Abstract

Optimal designs for models incorporating the Richards function, which are based on criteria involving a second-order approximation to the mean square error of estimates of the parameters, are considered. Interest focuses on the shape parameter and the asymptote of the model, and it is shown that designs which, separately, minimize the mean square error for estimates of these parameters exhibit severe nonlinearity, but that designs which minimize compromise criteria, or designs formed as a combination of optimal designs for the individual parameters, have attractive properties.