On the Choice of a Prior for Bayesian D-Optimal Designs for the Logistic Regression Model with a Single Predictor |
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Authors: | Haftom T. Abebe Frans E. S. Tan Gerard J. P. Van Breukelen Jan Serroyen Martijn P. F. Berger |
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Affiliation: | Department of Methodology and Statistics , Maastricht University , Maastricht , The Netherlands |
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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. |
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Keywords: | Bayesian D-optimal designs Locally D-optimal designs Logistic regression model Maximin Bayesian D-optimal design Relative efficiency |
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