Zero-inflated models with application to spatial count data |
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Authors: | Deepak K. Agarwal Alan E. Gelfand Steven Citron-Pousty |
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Affiliation: | (1) AT&T Shannon Research Labs, Florham Park, NJ, 07932-0971;(2) Institute of Statistics and Decision Sciences, Duke University, Durham, NC, 27708-0251;(3) Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, 06269 |
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Abstract: | Count data arises in many contexts. Here our concern is with spatial count 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. |
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Keywords: | conditionally autoregressive prior Langevin diffusions latent variables posterior propriety |
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