Full Bayesian Significance Test for Zero-Inflated Distributions.

In this article, we discuss an application of the Full Bayesian Significance Test (FBST) introduced by Pereira and Stern (1999) to compute the evidence of the Poisson distribution against the Zero-Inflated Poisson distribution (ZIP). The FBST is intuitive and easy to implement via Winbugs as an alternative to the classical tests formulated by Xie et al. (2001) in statistical process context. This evidence measure is based on the augmented data and used to test the fitting of the ZIP model for count data with excess of zeros in two illustrative examples in the statistical process control and the horticultural research. [ABSTRACT FROM AUTHOR]