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http://dx.doi.org/10.7465/jkdi.2015.26.2.495

Review on statistical methods for large spatial Gaussian data  

Park, Jincheol (Department of Statistics, Keimyung University)
Publication Information
Journal of the Korean Data and Information Science Society / v.26, no.2, 2015 , pp. 495-504 More about this Journal
Abstract
The Gaussian geostatistical model has been widely used for modeling spatial data. However, this model suffers from a severe difficulty in computation because inference requires to invert a large covariance matrix in evaluating log-likelihood. In addressing this computational challenge, three strategies have been employed: likelihood approximation, lower dimensional space approximation, and Markov random field approximation. In this paper, we reviewed statistical approaches attacking the computational challenge. As an illustration, we also applied integrated nested Laplace approximation (INLA) technology, one of Markov approximation approach, to real data to provide an example of its use in practice dealing with large spatial data.
Keywords
Gaussian field; Gaussian Markov field; integrated nested Laplace approximation;
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