Modeling Point Referenced Spatial Count Data: A Poisson Process Approach
نویسندگان
چکیده
Random fields are useful mathematical tools for representing natural phenomena with complex dependence structures in space and/or time. In particular, the Gaussian random field is commonly used due to its attractive properties and tractability. However, this assumption seems be restrictive when dealing counting data. To deal situation, we propose a Poisson marginal distribution considering sequence of independent copies an exponential as “inter-arrival times” renewal processes framework. Our proposal can viewed spatial generalization process. Unlike classical hierarchical Log-Gaussian model, our generates (non)-stationary that mean square continuous distributions. For proposed field, analytic expressions covariance function bivariate provided. extensive simulation study, investigate weighted pairwise likelihood method estimating parameters. Finally, effectiveness methodology illustrated by analysis reindeer pellet-group survey data, where zero-inflated version model compared copula models. Supplementary materials article, including technical proofs R code reproducing work, available online supplement.
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ژورنال
عنوان ژورنال: Journal of the American Statistical Association
سال: 2022
ISSN: ['0162-1459', '1537-274X', '2326-6228', '1522-5445']
DOI: https://doi.org/10.1080/01621459.2022.2140053