TY - JOUR
T1 - On the Choice of a Prior for Bayesian D-Optimal Designs for the Logistic Regression Model with a Single Predictor
AU - Abebe, H.T.
AU - Tan, F.E.S.
AU - van Breukelen, G.J.P.
AU - Serroyen, J.
AU - Berger, M.P.F.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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.
AB - 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.
U2 - 10.1080/03610918.2012.745556
DO - 10.1080/03610918.2012.745556
M3 - Article
SN - 0361-0918
VL - 43
SP - 1811
EP - 1824
JO - Communications in Statistics-Simulation and Computation
JF - Communications in Statistics-Simulation and Computation
IS - 7
ER -