G. Jona Lasinio, G. Mastrantonio, A. Pollice
When a large amount of spatial data is available computational and modeling challenges arise and they are often labeled as “big n problem”. In this work we present a brief review of the literature. Then we focus on two approaches, respectively based on Stochastic Partial Differential Equations and INLA, and on the tapering of the spatial covariance matrix. The fitting and predictive abilities of using the two methods in conjunction with Kriging interpolation are compared in a simulation study.
SPDE, INLA, tapering, Large Spatial Data Sets, Spatial Statistics
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