Geostatistical mixed beta regression: a Bayesian approach

摘要

This paper develops regression techniques for geostatistical data, with an emphasis in proportions measured on a continuous scale. Specifically, it deals with Beta regression models with mixed effects to control the spatial variability from a Bayesian approach. We use a suitable parametrization of the Beta distribution in terms of its mean and the precision parameter, allowing for both parameters to be modeled through regression structures that may involve fixed and random effects. Specification of prior distributions is discussed, computational implementation via Gibbs sampling is provided, and the methodology is illustrated using simulated and real data.