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Zero‐modified Poisson model: Bayesian approach, influence diagnostics, and an application to a Brazilian leptospirosis notification data
Author(s) -
Conceição Katiane S.,
Andrade Marinho G.,
Louzada Francisco
Publication year - 2013
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201100175
Subject(s) - contingency table , bayesian probability , count data , poisson distribution , zero inflated model , poisson regression , divergence (linguistics) , categorical variable , statistics , bayesian inference , computer science , mathematics , data mining , medicine , population , linguistics , philosophy , environmental health
In this paper, a Bayesian method for inference is developed for the zero‐modified Poisson (ZMP) regression model. This model is very flexible for analyzing count data without requiring any information about inflation or deflation of zeros in the sample. A general class of prior densities based on an information matrix is considered for the model parameters. A sensitivity study to detect influential cases that can change the results is performed based on the Kullback–Leibler divergence. Simulation studies are presented in order to illustrate the performance of the developed methodology. Two real datasets on leptospirosis notification in Bahia State (Brazil) are analyzed using the proposed methodology for the ZMP model.