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

• Autor(a) principal: Barriga, Gladys D. Cacsire [UNESP]
• Data de Publicação: 2016
• Outros Autores: Dey, Dipak K.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1080/03610926.2014.919397; http://hdl.handle.net/11449/159083
• topic: Bayesian analysis; Count data; Divergence measures; Generalized power series model; Parameter estimation; Regression model; Zero-inflated model

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.