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On the Choice of a Prior for Bayesian D-Optimal Designs for the Logistic Regression Model with a Single Predictor
Authors:Haftom T. Abebe  Frans E. S. Tan  Gerard J. P. Van Breukelen  Jan Serroyen  Martijn P. F. Berger
Affiliation:Department of Methodology and Statistics , Maastricht University , Maastricht , The Netherlands
Abstract:
The Bayesian design approach accounts for uncertainty of the parameter values on which optimal design depends, but Bayesian designs themselves depend on the choice of a prior distribution for the parameter values. This article investigates Bayesian D-optimal designs for two-parameter logistic models, using numerical search. We show three things: (1) a prior with large variance leads to a design that remains highly efficient under other priors, (2) uniform and normal priors lead to equally efficient designs, and (3) designs with four or five equidistant equally weighted design points are highly efficient relative to the Bayesian D-optimal designs.
Keywords:Bayesian D-optimal designs  Locally D-optimal designs  Logistic regression model  Maximin Bayesian D-optimal design  Relative efficiency
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