Identifying locally optimal designs for nonlinear models: A simple extension with profound consequences
Statistics Theory
2012-10-04 v1 Statistics Theory
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.
Cite
@article{arxiv.1210.1058,
title = {Identifying locally optimal designs for nonlinear models: A simple extension with profound consequences},
author = {Min Yang and John Stufken},
journal= {arXiv preprint arXiv:1210.1058},
year = {2012}
}
Comments
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)