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Bayesian inference in measurement error models for replicated data


M. de Castro, Universidade de São Paulo, Instituto de Ciências Matemáticas e de Computação, Av. Trabalhador São-carlense, 400, São Carlos-SP, Brazil. E-mail:


This paper deals with Bayesian inference in measurement error models with unknown error covariances. Our formulation covers heteroscedastic and homoscedastic models for replicated data. Both equation-error and no-equation-error models are included in our proposal. Resorting to data augmentation, we present a simulation-based framework using the Gibbs sampler. Model selection is also briefly discussed. Results from a simulation study are reported. We work out an illustrative example with a real data set on measurements of mineral element contents in pottery samples. Copyright © 2012 John Wiley & Sons, Ltd.

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