A Case Study Competition Among Methods for Analyzing Large Spatial Data
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DOI: 10.1007/s13253-018-00348-w
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- Wenceslao González‐Manteiga & Rosa M. Crujeiras & Daniel Simpson & Finn Lindgren & Håvard Rue, 2012. "In order to make spatial statistics computationally feasible, we need to forget about the covariance function," Environmetrics, John Wiley & Sons, Ltd., vol. 23(1), pages 65-74, February.
- Brian Neelon & Alan E. Gelfand & Marie Lynn Miranda, 2014. "A multivariate spatial mixture model for areal data: examining regional differences in standardized test scores," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(5), pages 737-761, November.
- Kang, Emily L. & Cressie, Noel, 2011. "Bayesian Inference for the Spatial Random Effects Model," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 972-983.
- Abhirup Datta & Sudipto Banerjee & Andrew O. Finley & Alan E. Gelfand, 2016. "Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 800-812, April.
- Gramacy, Robert B., 2016. "laGP: Large-Scale Spatial Modeling via Local Approximate Gaussian Processes in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i01).
- Kaufman, Cari G. & Schervish, Mark J. & Nychka, Douglas W., 2008. "Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1545-1555.
- Sudipto Banerjee & Alan E. Gelfand & Andrew O. Finley & Huiyan Sang, 2008. "Gaussian predictive process models for large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 825-848, September.
- Furrer, Reinhard & Sain, Stephan R., 2010. "spam: A Sparse Matrix R Package with Emphasis on MCMC Methods for Gaussian Markov Random Fields," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i10).
- Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
- Kang, Emily L. & Liu, Desheng & Cressie, Noel, 2009. "Statistical analysis of small-area data based on independence, spatial, non-hierarchical, and hierarchical models," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3016-3032, June.
- Faming Liang & Yichen Cheng & Qifan Song & Jincheol Park & Ping Yang, 2013. "A Resampling-Based Stochastic Approximation Method for Analysis of Large Geostatistical Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 325-339, March.
- Fuentes, Montserrat, 2007. "Approximate Likelihood for Large Irregularly Spaced Spatial Data," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 321-331, March.
- Huiyan Sang & Jianhua Z. Huang, 2012. "A full scale approximation of covariance functions for large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(1), pages 111-132, January.
- Toshihiro Hirano & Yoshihiro Yajima, 2013.
"Covariance tapering for prediction of large spatial data sets in transformed random fields,"
Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(5), pages 913-939, October.
- Toshihiro Hirano & Yoshihiro Yajima, 2011. "Covariance Tapering for Prediction of Large Spatial Data Sets in Transformed Random Fields," CIRJE F-Series CIRJE-F-823, CIRJE, Faculty of Economics, University of Tokyo.
- Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
- Leonhard Knorr-Held & Günter Raßer, 2000. "Bayesian Detection of Clusters and Discontinuities in Disease Maps," Biometrics, The International Biometric Society, vol. 56(1), pages 13-21, March.
- Matthias Katzfuss, 2017. "A Multi-Resolution Approximation for Massive Spatial Datasets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 201-214, January.
- Paciorek, Christopher J. & Lipshitz, Benjamin & Zhuo, Wei & Prabhat, . & Kaufman, Cari G. G. & Thomas, Rollin C., 2015. "Parallelizing Gaussian Process Calculations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i10).
- Michael L. Stein & Zhiyi Chi & Leah J. Welty, 2004. "Approximating likelihoods for large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(2), pages 275-296, May.
- Matthias Katzfuss & Noel Cressie, 2011. "Spatio‐temporal smoothing and EM estimation for massive remote‐sensing data sets," Journal of Time Series Analysis, Wiley Blackwell, vol. 32, pages 430-446, July.
- Furrer, Reinhard & Bachoc, François & Du, Juan, 2016. "Asymptotic properties of multivariate tapering for estimation and prediction," Journal of Multivariate Analysis, Elsevier, vol. 149(C), pages 177-191.
- Finn Lindgren & Håvard Rue & Johan Lindström, 2011. "An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 423-498, September.
- Finley, Andrew O. & Sang, Huiyan & Banerjee, Sudipto & Gelfand, Alan E., 2009. "Improving the performance of predictive process modeling for large datasets," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2873-2884, June.
- Kim, Hyoung-Moon & Mallick, Bani K. & Holmes, C.C., 2005. "Analyzing Nonstationary Spatial Data Using Piecewise Gaussian Processes," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 653-668, June.
- Lemos, Ricardo T. & Sansó, Bruno, 2009. "A Spatio-Temporal Model for Mean, Anomaly, and Trend Fields of North Atlantic Sea Surface Temperature," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 5-18.
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Keywords
Big data; Gaussian process; Parallel computing; Low-rank approximation;All these keywords.
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