Estimation and influence diagnostics for zero-inflated hyper-Poisson regression model: full Bayesian analysis

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Taylor & Francis Inc


The purpose of this paper is to develop a Bayesian analysis for the zero-inflated hyper-Poisson model. Markov chain Monte Carlo methods are used to develop a Bayesian procedure for the model and the Bayes estimators are compared by simulation with the maximum-likelihood estimators. Regression modeling and model selection are also discussed and case deletion influence diagnostics are developed for the joint posterior distribution based on the functional Bregman divergence, which includes -divergence and several others, divergence measures, such as the Itakura-Saito, Kullback-Leibler, and (2) divergence measures. Performance of our approach is illustrated in artificial, real apple cultivation experiment data, related to apple cultivation.



Bayesian inference, hyper-Poisson distribution, Kullback-Leibler divergence, zero-inflated models

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Communications In Statistics-theory And Methods. Philadelphia: Taylor & Francis Inc, v. 47, n. 11, p. 2741-2759, 2018.