A spatial zero-inflated poisson regression model for oak regeneration |
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Authors: | Stephen L Rathbun Songlin Fei |
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Institution: | (1) Department of Health Administration, Biostatistics and Epidemiology, University of Georgia, Athens, GA 30605, USA;(2) Department of Forestry, University of Kentucky, Lexington, KY 40546, USA |
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Abstract: | Ecological counts data are often characterized by an excess of zeros and spatial dependence. Excess zeros can occur in regions
outside the range of the distribution of a given species. A zero-inflated Poisson regression model is developed, under which
the species range is determined by a spatial probit model, including physical variables as covariates. Within that range,
species counts are independently drawn from a Poisson distribution whose mean depends on biotic variables. Bayesian inference
for this model is illustrated using data on oak seedling counts.
Received: May 2004 / Revised: December 2004 |
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Keywords: | Bayesian hierarchical spatial Model MCMC algorithm Spatial probit model |
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