Local and Bayesian approaches to optimal experimental design specify information about the regression parameters through a nominal value and a prior distribution, respectively. A practical question is whether accounting for parameter uncertainty justifies replacing a locally optimal design. We investigate when locally D-optimal designs remain Bayesian D-optimal in single-factor Poisson, gamma and logistic regression on the unit interval. Analytical arguments and numerical comparisons under normal and uniform slope priors examine how prior form and dispersion affect the optimal support and the Bayesian D-efficiency of retaining a local design.

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