CHINESE JOURNAL OF APPLIED PROBABILITY AND STATIST 2012, 28(5) 551-560 DOI:      ISSN: 1001-4268 CN: 31-1256

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Bayesian Inference of Hierarchical Regression Model for zero-Inflated Clustered Count Data

Shi Hongxing

School of Primary Education, Chuxiong Normal University

Abstract��

Zero-inflated Poisson (ZIP) regression
model is a popular tool for analyzing count data with excess zeros.
In this paper, a flexible hierarchical ZIP regression model is
proposed to handle with such data with cluster and Bayesian approach
is develop. A Gibbs sampler is employed to produce the Bayesian
estimate, a goodness-of-fit and a Bayesian information criterion
(BIC) are used for model comparison and selection. Finally, an
application of data from a ship damage incident study illustrates
the proposed method.

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