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Bayesian inference in measurement error models for replicated data
Author(s) -
Castro Mário,
Bolfarine Heleno,
Galea M.
Publication year - 2013
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.2179
Subject(s) - homoscedasticity , heteroscedasticity , inference , gibbs sampling , computer science , bayesian probability , bayesian inference , data set , observational error , model selection , statistics , mathematics , econometrics , algorithm , artificial intelligence
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.