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

TL;DR

This paper extends a method for finding locally optimal experimental designs in nonlinear models, enabling broader application and often resulting in designs with minimal support points, significantly impacting statistical design theory.

Contribution

It introduces a simple extension to existing methods, allowing for optimal designs under more criteria and for a wider class of models, with practical benefits.

Findings

Achieved locally optimal designs with minimal support points.

Extended applicability to various optimality criteria.

Demonstrated profound practical implications of the extension.

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.