Bayesian D‐optimal designs for error‐in‐variables models‡. (10th February 2017) - Konstantinou, Maria; Dette, Holger
Abstract : Bayesian optimality criteria provide a robust design strategy to parameter misspecification. We develop an approximate design theory for Bayesian D ‐optimality for nonlinear regression models with covariates subject to measurement errors. Both maximum likelihood and least squares estimation are studied, and explicit characterisations of the Bayesian D ‐optimal saturated designs for the Michaelis–Menten, Emax and exponential regression models are provided. Several data examples are considered for the case of no preference for specific parameter values, where Bayesian D ‐optimal saturated designs are calculated using the uniform prior and compared with several other designs, including the corresponding locally D ‐optimal designs, which are often used in practice. Copyright © 2017 John Wiley & Sons, Ltd.