Discussion on “Competition on Spatial Statistics for Large Datasets” View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2021-07-23

AUTHORS

Denis Allard, Lucia Clarotto, Thomas Opitz, Thomas Romary

ABSTRACT

We 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... »

PAGES

604-611

References to SciGraph publications

  • 2021-03-03. Gaussian process learning via Fisher scoring of Vecchia’s approximation in STATISTICS AND COMPUTING
  • 2021-07-08. Competition on Spatial Statistics for Large Datasets in JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s13253-021-00462-2

    DOI

    http://dx.doi.org/10.1007/s13253-021-00462-2

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1139874912

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/34335011


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