WinBUGS – A Bayesian modelling framework: Concepts, structure, and extensibility
Statistics and Computing
A comparative study of Gaussian geostatistical models and Gaussian Markov random field models
Journal of Multivariate Analysis
Editorial: Spatial statistics: Methods, models & computation
Computational Statistics & Data Analysis
Bayesian spatial models with a mixture neighborhood structure
Journal of Multivariate Analysis
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A spatial process observed over a lattice or a set of irregular regions is usually modeled using a conditionally autoregressive (CAR) model. The neighborhoods within a CAR model are generally formed deterministically using the inter-distances or boundaries between the regions. An extension of CAR model is proposed in this article where the selection of the neighborhood depends on unknown parameter(s). This extension is called a Stochastic Neighborhood CAR (SNCAR) model. The resulting model shows flexibility in accurately estimating covariance structures for data generated from a variety of spatial covariance models. Specific examples are illustrated using data generated from some common spatial covariance functions as well as real data concerning radioactive contamination of the soil in Switzerland after the Chernobyl accident.