Estimation and influence diagnostics for zero-inflated hyper-Poisson regression model: full Bayesian analysis |
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| Authors: | Vicente G Cancho Bao Yiqi Jose A Fiorucci Gladys D C Barriga Dipak K Dey |
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| Institution: | 1. Sciences institute of mathematics and computers, University of S?o Paulo, Brazilgaribay@icmc.usp.br;3. Sciences institute of mathematics and computers, University of S?o Paulo, Brazil;4. Department of Statistics, Federal University of S?o Carlos, S?o Carlos, Brazil;5. Production Engineering Department, S?o Paulo State University, Bauru, Brazil;6. Department of Statistics, University of Connecticut, Storrs, USA |
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| Abstract: | 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 ψ-divergence and several others, divergence measures, such as the Itakura–Saito, Kullback–Leibler, and χ2 divergence measures. Performance of our approach is illustrated in artificial, real apple cultivation experiment data, related to apple cultivation. |
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| Keywords: | Bayesian inference hyper-Poisson distribution Kullback–Leibler divergence zero-inflated models |
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