Identifying locally optimal designs for nonlinear models: A simple extension with profound consequences

Abstract

We extend the approach in [Ann. Statist. 38 (2010) 2499–2524] for identifying locally optimal designs for nonlinear models. Conceptually the extension is relatively simple, but the consequences in terms of applications are profound. As we will demonstrate, we can obtain results for locally optimal designs under many optimality criteria and for a larger class of models than has been done hitherto. In many cases the results lead to optimal designs with the minimal number of support points.