A Bayesian semiparametric model for non negative semicontinuous data. Issue 10 (19th May 2017)

• Record Type: Journal Article
• Authors: Dreassi, Emanuela; Rocco, Emilia
• Is Part Of: Communications in statistics. Volume 46:Issue 10(2017)
• Page Start: 5133
• Page End: 5146
• Publication Date: 2017-05-19
• Subjects: Dirichlet processes -- Hierarchical Bayesian models -- Small area estimation -- Two-part models.; 62G08 -- 62F15; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2015.1096389
• Languages: English
• ISSNs: 0361-0926

Abstract

ABSTRACT: When the target variable exhibits a semicontinuous behavior (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 article, a semiparametric Bayesian two-part 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 modeling and predicting semicontinuous data when parametric assumptions are not reasonable.