Abstract
AbstractWe discuss the experiences and results of the AppStatUZH team’s participation in the comprehensive and unbiased comparison of different spatial approximations conducted in the Competition for Spatial Statistics for Large Datasets. In each of the different sub-competitions, we estimated parameters of the covariance model based on a likelihood function and predicted missing observations with simple kriging. We approximated the covariance model either with covariance tapering or a compactly supported Wendland covariance function.
Funder
swiss national science foundation
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Statistics, Probability and Uncertainty,General Agricultural and Biological Sciences,Agricultural and Biological Sciences (miscellaneous),General Environmental Science,Statistics and Probability
Reference10 articles.
1. Bevilacqua M, Faouzi T, Furrer R, Porcu E (2019) Estimation and prediction using generalized Wendland covariance functions under fixed domain asymptotics. Ann Stat 47:828–856
2. Furrer R, Sain SR (2010) spam: a sparse matrix R package with emphasis on MCMC methods for Gaussian Markov random fields. J Stat Softw 36:1–25
3. Furrer R, Genton MG, Nychka D (2006) Covariance tapering for interpolation of large spatial datasets. J Comput Graph Stat 15:502–523
4. Furrer R, Flury R, Gerber F (2020) spam: SPArse Matrix. R package version 2.6, https://CRAN.R-project.org/package=spam
5. Furrer R, Flury R, Gerber F (2020) spam64: 64-Bit Extension of the SPArse Matrix R Package ‘spam’. R package version 2.6, https://CRAN.R-project.org/package=spam64
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