A Bayesian Approach to Parameter Estimation in Simplex Regression Model: A Comparison with Beta Regression

Abstract

Some variables are restricted to the open interval (0,1) and several methods have been developed to work with them under the scheme of the regression analysis. Most of research consider maximum likelihood methods and the use of Beta or Simplex distributions. This paper presents the use of Bayesian techniques to estimate the parameters of the simplex regression supported on the implementation of some simulations and a comparison with Beta regression. We consider both models with constant variance and models with variance heterogeneity. Regressions are exemplified with heteroscedasticity.

Keywords

• distribución beta
• distribución simplex
• muestreador de Gibbs
• proporciones
• varianza heterogénea