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Journal of Probability and Statistics
Volume 2012, Article ID 436239, 15 pages
http://dx.doi.org/10.1155/2012/436239
Research Article

Two-Stage Adaptive Optimal Design with Fixed First-Stage Sample Size

Department of Statistics, University of Missouri, 146 Middlebush Hall, Columbia, MO 65203, USA

Received 1 June 2012; Accepted 5 September 2012

Academic Editor: Zhengjia Chen

Copyright © 2012 Adam Lane and Nancy Flournoy. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In adaptive optimal procedures, the design at each stage is an estimate of the optimal design based on all previous data. Asymptotics for regular models with fixed number of stages are straightforward if one assumes the sample size of each stage goes to infinity with the overall sample size. However, it is not uncommon for a small pilot study of fixed size to be followed by a much larger experiment. We study the large sample behavior of such studies. For simplicity, we assume a nonlinear regression model with normal errors. We show that the distribution of the maximum likelihood estimates converges to a scale mixture family of normal random variables. Then, for a one parameter exponential mean function we derive the asymptotic distribution of the maximum likelihood estimate explicitly and present a simulation to compare the characteristics of this asymptotic distribution with some commonly used alternatives.