Bayesian inference and diagnostics in zero-inflated generalized power series regression model
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Undergraduate course
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Publisher
Taylor & Francis Inc
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Article
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Acesso aberto
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Abstract
The 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 -divergence, which includes several divergence measures such as the Kullback-Leibler, J-distance, L-1 norm, and (2)-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.
Description
Keywords
Bayesian analysis, Count data, Divergence measures, Generalized power series model, Parameter estimation, Regression model, Zero-inflated model
Language
English
Citation
Communications In Statistics-theory And Methods. Philadelphia: Taylor & Francis Inc, v. 45, n. 22, p. 6553-6568, 2016.
