Bayesian spatial prediction |
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Authors: | Gaudard Marie Karson Marvin Linder Ernst Sinha Debajyoti |
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Institution: | (1) Department of Mathematics and Statistics, University of New Hampshire, Durham, New Hampshire;(2) Department of Decision Sciences, University of New Hampshire, Durham, New Hampshire |
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Abstract: | This paper presents a complete Bayesian methodology for analyzing spatial data, one which employs proper priors and features diagnostic methods in the Bayesian spatial setting. The spatial covariance structure is modeled using a rich class of covariance functions for Gaussian random fields. A general class of priors for trend, scale, and structural covariance parameters is considered. In particular, we obtain analytic results that allow easy computation of the predictive distribution for an arbitrary prior on the parameters of the covariance function using importance sampling. The computations, as well as model diagnostics and sensitivity analysis, are illustrated with a set of precipitation data. |
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Keywords: | spatial interpolation Bayesian model universal kriging model diagnostics sensitivity analysis |
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