Assessing Heterogeneity of Two-Part Model via Bayesian Model-Based Clustering with Its Application to Cocaine Use Data

The purpose of this chapter is to provide an introduction the model-based clustering within Bayesian framework and apply it asses heterogeneity fractional data via finite mixture two-part regression model. problems related number clusters configuration observations are addressed Markov Chains Monte Carlo (MCMC) sampling method. Gibbs sampler implemented draw from full conditionals. As a concrete example, cocaine use analyzed illustrate merits proposed methodology.