Optimal Bayesian Design for a Logistic Regression Model: Geometric
and Algebraic Approaches
by Marilyn Agin
and
Kathryn Chaloner
Technical Report No. 622
School of Statistics
University of Minnesota
August, 1997
Abstract
A simple logistic regression model with a known slope parameter provides
a simple model for understanding optimal Bayesian designs. For this
model the geometric approach of Haines (1995) and the algebraic approach
of Chaloner (1993) for finding optimal designs for a prior distribution
with just two support points are reviewed, compared and discussed. A
result is given, using the algebraic approach, for a three point prior
distribution and difficulties of the geometric approach are illustrated.
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