Ontology type: schema:ScholarlyArticle Open Access: True
2021-07-23
AUTHORSDenis Allard, Lucia Clarotto, Thomas Opitz, Thomas Romary
ABSTRACTWe discuss the methods and results of the RESSTE team in the competition on spatial statistics for large datasets. In the first sub-competition, we implemented block approaches both for the estimation of the covariance parameters and for prediction using ordinary kriging. In the second sub-competition, a two-stage procedure was adopted. In the first stage, the marginal distribution is estimated neglecting spatial dependence, either according to the flexible Tuckey g and h distribution or nonparametrically. In the second stage, estimation of the covariance parameters and prediction are performed using Kriging. Vecchias’s approximation implemented in the GpGp package proved to be very efficient. We then make some propositions for future competitions. More... »
PAGES604-611
http://scigraph.springernature.com/pub.10.1007/s13253-021-00462-2
DOIhttp://dx.doi.org/10.1007/s13253-021-00462-2
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1139874912
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/34335011
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