摘要：In this paper, we introduce a Bayesian extended regression model
with two-stage priors when the covariate is positive and measured with error.
Connections are made with some results in Arellano-Valle and Azzalini (2006), re-
lated to the multivariate skew-normal distributions. The usefulness of the pro-
posed model with errors in variables, via the two-stage priors formulated by
O'Hagan and Leonard (1976), is illustrated with an example abstracted from Fuller
(1987, pg. 18). The main advantage of this extended Bayesian approach is the
use of skewed priors, typically rare in most Bayesian applications, and to treat
the true value of the explanatory variable as positive, consideration that is some-
times ignored in measurement error models. Such consideration makes naturally
the model identi able, a problem that signi cantly has troubled users of other ap-
proaches listed in the literature. This constraint implies also a strong asymmetry
in the distribution of the response variable. Strong connections are shown with
results in Copas and Li (1997) on non-random samples and with Berkson models,
which are important in practical applications. Extensions of Copas and Li's results
for models with vector explanatory variables are presented.
