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• W2022986957 abstract "The Gaussian geostatistical model has been widely used for modeling spatial data. However, this model suffers from a severe difficulty in computation: it requires users to invert a large covariance matrix. This is infeasible when the number of observations is large. In this article, we propose an auxiliary lattice-based approach for tackling this difficulty. By introducing an auxiliary lattice to the space of observations and defining a Gaussian Markov random field on the auxiliary lattice, our model completely avoids the requirement of matrix inversion. It is remarkable that the computational complexity of our method is only O(n), where n is the number of observations. Hence, our method can be applied to very large datasets with reasonable computational (CPU) times. The numerical results indicate that our model can approximate Gaussian random fields very well in terms of predictions, even for those with long correlation lengths. For real data examples, our model can generally outperform conventional Gaussian random field models in both prediction errors and CPU times. Supplemental materials for the article are available online." @default.
• W2022986957 created "2016-06-24" @default.
• W2022986957 creator A5017518380 @default.
• W2022986957 creator A5085287370 @default.
• W2022986957 date "2012-04-01" @default.
• W2022986957 modified "2023-09-23" @default.
• W2022986957 title "Bayesian Analysis of Geostatistical Models With an Auxiliary Lattice" @default.
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• W2022986957 doi "https://doi.org/10.1080/10618600.2012.679228" @default.
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