A Bayesian semiparametric model for semicontinuous data

TL;DR

This paper introduces a Bayesian semiparametric two-part regression model using Dirichlet processes to effectively model semicontinuous data, especially when traditional parametric assumptions are inadequate.

Contribution

It proposes a novel Bayesian semiparametric approach for semicontinuous data using Dirichlet processes, enhancing flexibility over existing parametric models.

Findings

Model performs well in small area estimation context.

Offers flexible modeling when parametric assumptions fail.

Demonstrates satisfactory prediction accuracy.

Abstract

When the target variable exhibits a semicontinuous behaviour (i.e. a point mass in a single value and a continuous distribution elsewhere) parametric `two-part regression models' have been extensively used and investigated. In this paper, a semiparametric Bayesian two-part regression model for dealing with such variables is proposed. The model allows a semiparametric expression for the two part of the model by using Dirichlet processes. A motivating example (in the `small area estimation' framework) based on pseudo-real data on grapewine production in Tuscany, is used to evaluate the capabilities of the model. Results show a satisfactory performance of the suggested approach to model and predict semicontinuous data when parametric assumptions (distributional and/or relationship) are not reasonable.