Bayesian inference and diagnostics in zero-inflated generalized power series regression model

Gladys D. Cacsire Barriga and Dipak K. Dey

Communications in Statistics - Theory and Methods, 2016, vol. 45, issue 22, 6553-6568

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 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.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:45:y:2016:i:22:p:6553-6568

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DOI: 10.1080/03610926.2014.919397

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