A Bayesian approach for some zero-modified Poisson mixture models. (October 2020)

• Record Type: Journal Article
• Authors: Bertoli, Wesley; Conceição, Katiane S; Andrade, Marinho G; Louzada, Francisco
• Is Part Of: Statistical modelling. Volume 20:Number 5(2020)
• Page Start: 467
• Page End: 501
• Publication Date: 2020-10
• Subjects: Bayesian inference -- model comparison -- Poisson mixture distributions -- zero-inflated/deflated data -- zero-modified models; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X19841984
• Languages: English
• ISSNs: 1471-082X

Abstract

In this article, we propose a class of zero-modified Poisson mixture models as an alternative to model overdispersed count data exhibiting inflation or deflation of zeros. A relevant feature of this class is that the zero modification can be incorporated using a zero truncation process and consequently, the proposed models can be expressed in the hurdle version. This procedure leads to the fact that the proposed models can be fitted without any previous information about the zero modification present in agiven dataset. A fully Bayesian approach has been considered for estimation and inference concerns. Three different simulation studies have been conducted to illustrate the performance of the developed methodology. The usefulness of the proposed class of models has been assessed by using three real datasets provided by the literature. A general model comparison with some well-known discrete distributions has been presented.