Robust beta regression modeling with errors-in-variables: a Bayesian approach and numerical applications

Beta regression models have become a popular tool for describing and predicting limited-range continuous data such as rates and proportions. However, these models can be severely affected by outlying observations that the beta distribution does not handle well. A robust alternative to the modeling w...

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