A Bayesian semiparametric model for non-negative semicontinuous data

When the target variable exhibits a semicontinuous behaviour (a point mass in a single value and a continuous distribution elsewhere) parametric ‘two-part models’ have been extensively used and investigated. The applications have mainly been related to non-negative variables with a point mass in zero (zero-inflated data). In this paper a semiparametric Bayesian twopart model for dealing with such variables is proposed. The model allows a semiparametric expression for the two parts of the model by using Dirichlet processes. A motivating example, based on grape wine production in Tuscany (an Italian region), is used to show the capabilities of the model. Finally, two simulation experiments evaluate the model. Results show a satisfactory performance of the suggested approach for modelling and predicting semicontinuous data when parametric assumptions are not reasonable.