Authors: Konstantinou, Maria
Dette, Holger
Title: Bayesian D-optimal designs for error-in-variables models
Language (ISO): en
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.
Subject Headings: error-in-variables models
Bayesian optimal designs
classical errors
Issue Date: 2016
Appears in Collections:Sonderforschungsbereich (SFB) 823

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