Fitting Gaussian Markov Random Fields to Gaussian Fields

Citations

Cited by:

1. Varin, Cristiano & Host, Gudmund & Skare, Oivind, 2005. "Pairwise likelihood inference in spatial generalized linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 1173-1191, June.
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3. Håvard Rue & Ingelin Steinsland & Sveinung Erland, 2004. "Approximating hidden Gaussian Markov random fields," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 877-892, November.
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5. Morales-Oñate, Víctor & Crudu, Federico & Bevilacqua, Moreno, 2021. "Blockwise Euclidean likelihood for spatio-temporal covariance models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 176-201.
   • Víctor Morales-Oñate & Federico Crudu & Moreno Bevilacqua, 2020. "Blockwise Euclidean likelihood for spatio-temporal covariance models," Department of Economics University of Siena 822, Department of Economics, University of Siena.
6. Peter W Gething & Anand P Patil & Simon I Hay, 2010. "Quantifying Aggregated Uncertainty in Plasmodium falciparum Malaria Prevalence and Populations at Risk via Efficient Space-Time Geostatistical Joint Simulation," PLOS Computational Biology, Public Library of Science, vol. 6(4), pages 1-12, April.
7. Giovanna Jona Lasinio & Gianluca Mastrantonio & Alessio Pollice, 2013. "Discussing the “big n problem”," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(1), pages 97-112, March.
8. Song, Hae-Ryoung & Fuentes, Montserrat & Ghosh, Sujit, 2008. "A comparative study of Gaussian geostatistical models and Gaussian Markov random field models," Journal of Multivariate Analysis, Elsevier, vol. 99(8), pages 1681-1697, September.
9. Cressie, Noel & Verzelen, Nicolas, 2008. "Conditional-mean least-squares fitting of Gaussian Markov random fields to Gaussian fields," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2794-2807, January.
10. Ying C. MacNab, 2018. "Rejoinder on: Some recent work on multivariate Gaussian Markov random fields," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 554-569, September.
11. White, Gentry & Ghosh, Sujit K., 2009. "A stochastic neighborhood conditional autoregressive model for spatial data," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3033-3046, June.
12. Margaret R Donald & Kerrie L Mengersen & Rick R Young, 2015. "A Four Dimensional Spatio-Temporal Analysis of an Agricultural Dataset," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
13. Stephan R. Sain & Reinhard Furrer, 2018. "Comments on: Some recent work on multivariate Gaussian Markov random fields," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 545-548, September.
14. Sandy Burden & Noel Cressie & David G. Steel, 2015. "The SAR Model for Very Large Datasets: A Reduced Rank Approach," Econometrics, MDPI, vol. 3(2), pages 1-22, May.
15. Angela Ferretti & L. Ippoliti & P. Valentini & R. J. Bhansali, 2023. "Long memory conditional random fields on regular lattices," Environmetrics, John Wiley & Sons, Ltd., vol. 34(5), August.
16. Vinicius Mayrink & Dani Gamerman, 2009. "On computational aspects of Bayesian spatial models: influence of the neighboring structure in the efficiency of MCMC algorithms," Computational Statistics, Springer, vol. 24(4), pages 641-669, December.
17. Hartman, Linda & Hossjer, Ola, 2008. "Fast kriging of large data sets with Gaussian Markov random fields," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2331-2349, January.
18. Litvinenko, Alexander & Sun, Ying & Genton, Marc G. & Keyes, David E., 2019. "Likelihood approximation with hierarchical matrices for large spatial datasets," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 115-132.
19. Steinsland, Ingelin, 2007. "Parallel exact sampling and evaluation of Gaussian Markov random fields," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 2969-2981, March.
20. Corentin M Barbu & Andrew Hong & Jennifer M Manne & Dylan S Small & Javier E Quintanilla Calderón & Karthik Sethuraman & Víctor Quispe-Machaca & Jenny Ancca-Juárez & Juan G Cornejo del Carpio & Fernan, 2013. "The Effects of City Streets on an Urban Disease Vector," PLOS Computational Biology, Public Library of Science, vol. 9(1), pages 1-9, January.
21. Peter Diggle & Soren Lophaven, 2004. "Bayesian Geostatistical Design," Johns Hopkins University Dept. of Biostatistics Working Paper Series 1042, Berkeley Electronic Press.
22. Paciorek, Christopher J., 2007. "Computational techniques for spatial logistic regression with large data sets," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3631-3653, May.
23. Hossein Boojari & Majid Khaledi & Firoozeh Rivaz, 2016. "A non-homogeneous skew-Gaussian Bayesian spatial model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 55-73, March.
24. Alex Diana & Emily Beth Dennis & Eleni Matechou & Byron John Treharne Morgan, 2023. "Fast Bayesian inference for large occupancy datasets," Biometrics, The International Biometric Society, vol. 79(3), pages 2503-2515, September.
25. Zahra Barzegar & Firoozeh Rivaz, 2020. "A scalable Bayesian nonparametric model for large spatio-temporal data," Computational Statistics, Springer, vol. 35(1), pages 153-173, March.
26. Bolin, David & Lindgren, Finn, 2013. "A comparison between Markov approximations and other methods for large spatial data sets," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 7-21.
27. Zammit-Mangion, Andrew & Rougier, Jonathan, 2018. "A sparse linear algebra algorithm for fast computation of prediction variances with Gaussian Markov random fields," Computational Statistics & Data Analysis, Elsevier, vol. 123(C), pages 116-130.
28. Isa Marques & Thomas Kneib & Nadja Klein, 2022. "Mitigating spatial confounding by explicitly correlating Gaussian random fields," Environmetrics, John Wiley & Sons, Ltd., vol. 33(5), August.