A Bayesian analysis of zero-inflated generalized Poisson model

Authors:
Jean-François Angers;Atanu Biswas
Affiliations:
Département de mathématiques et de statistique, Université de Montréal, C.P. 6128 Succ. "Centre-ville", Montréal, Qué, Canada H3C 3J7;Applied Statistics Unit, Indian Statistical Institute, 203 B.T. Road, Kolkata 700 108, India
Venue:
Computational Statistics & Data Analysis
Year:
2003

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Abstract

In several real-life examples one encounters count data where the number of zeros is such that the usual Poisson distribution does not fit the data. Quite often the number of zeros is large, and hence the data is zero inflated. In this situation, a zero-inflated generalized Poisson model can be considered and a Bayesian analysis can be carried out. Some appropriate priors are discussed and the posteriors are obtained using Monte-Carlo integration with importance sampling. The predictive density of the future observation is also obtained. The techniques are illustrated using a real-life data set. Computations largely support the methodology.