A comparative study of Gaussian geostatistical models and Gaussian Markov random field models

DOI10.1016/j.jmva.2008.01.012zbMath1142.86309OpenAlexW2035763857WikidataQ37143689 ScholiaQ37143689MaRDI QIDQ943606

Hae-Ryoung Song, Montserrat Fuentes, Sujit Kumar Ghosh

Publication date: 10 September 2008

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc2662683

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Geostatistics (86A32) Environmental economics (natural resource models, harvesting, pollution, etc.) (91B76)

Related Items

Sampling Strategies for Fast Updating of Gaussian Markov Random Fields, A comparison between Markov approximations and other methods for large spatial data sets, Maximum likelihood estimation for generalized conditionally autoregressive models of spatial data, Validation and comparison of geostatistical and spline models for spatial stream networks, Spatial models for point and areal data using Markov random fields on a fine grid, THE CORRELATION STRUCTURE OF SPATIAL AUTOREGRESSIONS, A stochastic neighborhood conditional autoregressive model for spatial data, Data-driven neighborhood selection of a Gaussian field, An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach, Adaptive estimation of stationary Gaussian fields, Maximum likelihood estimation for directional conditionally autoregressive models

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