On the Choice of a Prior for Bayesian D-Optimal Designs for the Logistic Regression Model with a Single Predictor

H.T. Abebe*, F.E.S. Tan, G.J.P. van Breukelen, J. Serroyen, M.P.F. Berger

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


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.
Original languageEnglish
Pages (from-to)1811-1824
JournalCommunications in Statistics-Simulation and Computation
Issue number7
Early online date16 Aug 2013
Publication statusPublished - 1 Jan 2014

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