Ontology type: schema:ScholarlyArticle Open Access: True
2021-07-24
AUTHORS 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. More... »
PAGES599-603
http://scigraph.springernature.com/pub.10.1007/s13253-021-00461-3
DOIhttp://dx.doi.org/10.1007/s13253-021-00461-3
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1139913578
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/34720575
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