Estimation and influence diagnostics for zero-inflated hyper-Poisson regression model: full Bayesian analysis
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 modeli...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | Brasil |
| Institución: | Universidade Estadual Paulista (UNESP) |
| Repositorio: | Repositório Institucional da UNESP |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unesp.br:11449/164037 |
| Acceso en línea: | http://dx.doi.org/10.1080/03610926.2017.1342839 http://hdl.handle.net/11449/164037 |
| Access Level: | acceso abierto |
| Palabra clave: | Bayesian inference hyper-Poisson distribution Kullback-Leibler divergence zero-inflated models |
| Sumario: | 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. |
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