A spatial zero-inflated poisson regression model for oak regeneration
作者： Stephen L. Rathbun , Songlin Fei
DOI: 10.1007/S10651-006-0020-X
关键词: Zero-inflated model 、 Econometrics 、 Poisson regression 、 Mathematics 、 Poisson distribution 、 Regression analysis 、 Spatial dependence 、 Probit model 、 Statistics 、 Species distribution 、 Generalized linear model
摘要: Ecological counts data are often characterized by an excess of zeros and spatial dependence. Excess can occur in regions outside the range dis- tribution a given species. A zero-inflated Poisson regression model is developed, under which species determined probit model, including physical variables as covariates. Within that range, independently drawn from distribution whose mean depends on biotic variables. Bayesian inference for this illustrated using oak seedling counts.
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