IDENTIFYING LOCALLY OPTIMAL DESIGNS FOR NONLINEAR MODELS: A SIMPLE EXTENSION WITH PROFOUND CONSEQUENCES

Min Yang · John Stufken

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Optimal Experimental Design Methods Statistical Methods in Clinical Trials Advanced Statistical Methods and Models

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.

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1966Optimal Experimental Designs 2006Design Issues for Generalized Linear Models: A Review 2010On the de la Garza phenomenon 2009Support Points of Locally Optimal Designs for Nonlinear Models with Two Parameters

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