Beta regression models have been widely used for the analysis of limited-range continuous variables. Here, we consider an extension of the beta regression models that allows for explanatory variables to be measured with error. Then we propose a Bayesian treatment for errors-in-variables beta regression models. The specification of prior distributions is discussed, computational implementation via Gibbs sampling is provided, and two real data applications are presented. Additionally, Monte Carlo simulations are used to evaluate the performance of the proposed approach.
"A Bayesian approach to errors-in-variables beta regression." Braz. J. Probab. Stat. 32 (3) 559 - 582, August 2018. https://doi.org/10.1214/17-BJPS354