A Bayesian semiparametric model for non negative semicontinuous data |
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Authors: | Emanuela Dreassi Emilia Rocco |
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Institution: | Dipartimento di Statistica, Informatica, Applicazioni (DiSIA), Università di Firenze, Viale Morgagni, Florence, Italy |
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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. |
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Keywords: | Dirichlet processes Hierarchical Bayesian models Small area estimation Two-part models |
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