  We present a proposal to deal with the non-normality issue in the context of
regression models with measurement errors when both the response and the
explanatory variable are observed with error. We extend the normal model by
jointly modeling the unobserved covariate and the random errors by a finite
mixture of scale mixture of skew-normal distributions. This approach allows us
to model data with great flexibility, accommodating skewness, heavy tails, and
multi-modality.
