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Zero-inflated models with application to spatial count data
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DOI：10.1023/A:1020910605990.png)
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Abstract
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Count data arises in many contexts. Here our concern is with spatial data which exhibit an excessive number of zeros. Using the class of zero-inflated count models provides a flexible way to address this problem. Available covariate information suggests formulation of such modeling within a regression framework. We employ zero-inflated Poisson regression models. Spatial association is introduced through suitable random effects yielding a hierarchical model. We propose fitting this model within a Bayesian framework considering issues of posterior propriety, informative prior specification and well-behaved simulation based model fitting. Finally, we illustrate the model fitting with a data set involving counts of isopod nest burrows for 1649 pixels over a portion of the Negev desert in Israel.
Keywords:
conditionally autoregressive prior
Langevin diffusions
latent variables
posterior propriety
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