Bayesian D-optimal designs for error-in-variables models
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Date
2016
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Abstract
Bayesian optimality criteria provide a robust design strategy to parameter misspeci-
fication. We develop an approximate design theory for Bayesian D-optimality for non-
linear 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 to several other designs, including the
corresponding locally D-optimal designs, which are often used in practice.
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Keywords
error-in-variables models, D-optimality, Bayesian optimal designs, classical errors