Bayesian inference and diagnostics in zero-inflated generalized power series regression model |
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| Authors: | Gladys D Cacsire Barriga Dipak K Dey |
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| Institution: | 1. Univesidade Estadual Paulista “Julio de Mesquita filho”–FEB, Av. Eng. Luiz Edmundo C. Coube, Bauru, SP, Brazilglad@feb.unesp.br;3. Department of Statistics, University of Connecticut, Storrs, CT, USA |
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| Abstract: | ABSTRACTThe paper provides a Bayesian analysis for the zero-inflated regression models based on the generalized power series distribution. The approach is based on Markov chain Monte Carlo methods. The residual analysis is discussed and case-deletion influence diagnostics are developed for the joint posterior distribution, based on the ψ-divergence, which includes several divergence measures such as the Kullback–Leibler, J-distance, L1 norm, and χ2-square in zero-inflated general power series models. The methodology is reflected in a data set collected by wildlife biologists in a state park in California. |
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| Keywords: | Bayesian analysis Count data Divergence measures Generalized power series model Parameter estimation Regression model Zero-inflated model |
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