Fast Bayesian inference of block Nearest Neighbor Gaussian models for large data

Zaida C. Quiroz, Marcos O. Prates, Dipak K. Dey, H. åvard Rue

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Resumen

This paper presents the development of a spatial block-Nearest Neighbor Gaussian process (blockNNGP) for location-referenced large spatial data. The key idea behind this approach is to divide the spatial domain into several blocks which are dependent under some constraints. The cross-blocks capture the large-scale spatial dependence, while each block captures the small-scale spatial dependence. The resulting blockNNGP enjoys Markov properties reflected on its sparse precision matrix. It is embedded as a prior within the class of latent Gaussian models, thus fast Bayesian inference is obtained using the integrated nested Laplace approximation. The performance of the blockNNGP is illustrated on simulated examples, a comparison of our approach with other methods for analyzing large spatial data and applications with Gaussian and non-Gaussian real data.

Idioma originalInglés
Número de artículo54
PublicaciónStatistics and Computing
Volumen33
N.º2
DOI
EstadoPublicada - abr. 2023

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