A Bayesian hierarchical beta regression model was developed for continuous data restricted to the interval (0, 1) and applied to household expenditure and foot-and-mouth disease virus datasets.
The study presents a Bayesian hierarchical regression model for analyzing continuous data restricted to the interval (0, 1), demonstrating its utility in diverse applications.
Summary There is considerable interest in understanding how factors such as time and geographic distance between isolates might influence the evolutionary direction of foot‐and‐mouth disease. Genetic differences between viruses can be measured as the proportion of nucleotides that differ for a given sequence or gene. We present a Bayesian hierarchical regression model for the statistical analysis of continuous data with sample space restricted to the interval (0, 1). The data are modelled using beta distributions with means that depend on covariates through a link function. We discuss methodology for: (i) the incorporation of informative prior information into an analysis; (ii) fitting the model using Markov chain Monte Carlo sampling; (iii) model selection using Bayes factors; and (iv) semiparametric beta regression using penalized splines. The model was applied to two different datasets.
Branscum et al. (Tue,) conducted a other in Foot-and-mouth disease. Bayesian hierarchical beta regression model was evaluated. A Bayesian hierarchical beta regression model was developed for continuous data restricted to the interval (0, 1) and applied to household expenditure and foot-and-mouth disease virus datasets.