Generalized Estimating Equations for Zero-Inflated Spatial Count Data

This paper consolidates the zero-inflated Poisson model for count data with excess zeros proposed by Lambert (1992) and the two-component model approach for serial correlation among repeated observations proposed by Dobbie and Welsh (2001) for spatial count data; not only does this address the problem of overdispersion, but additionally provides for greater flexibility within the zero components, allowing for the distinction between zeros that arise due to random sampling and those that arise due to an inherent characteristic that may induce zero observations. A likelihood and corresponding score equations are derived for the zero-inflated Poisson model; spatial correlation may be incorporated via any spatial correlation structure following Diggle et al. (2009). A Matérn (1960) correlation is implemented as an illustrative example.