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.

Published in at http://dx.doi.org/10.1214/12-AOS992 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (3)

• Design Issues for Generalized Linear Models: A Review
• Support points of locally optimal designs for nonlinear models with two parameters
• A note on the de la Garza phenomenon for locally optimal designs

Cited by in corpus (4)

• Complete classes of designs for nonlinear regression models and principal representations of moment spaces
• Optimal group testing designs for estimating prevalence with uncertain testing errors
• Saturated locally optimal designs under differentiable optimality criteria
• On optimal designs for non-regular models