Discussion on “Competition on Spatial Statistics for Large Datasets”

Denis Allard (), Lucia Clarotto, Thomas Opitz and Thomas Romary
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Denis Allard: Biostatistics and Spatial Processes (BioSP), INRAE
Lucia Clarotto: PSL University, Centre de Geosciences
Thomas Opitz: Biostatistics and Spatial Processes (BioSP), INRAE
Thomas Romary: PSL University, Centre de Geosciences

Journal of Agricultural, Biological and Environmental Statistics, 2021, vol. 26, issue 4, No 7, 604-611

Abstract: 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.

Keywords: Composite likelihood; Block likelihood; Block approach; Tuckey g and h; Vecchia’s approximation (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s13253-021-00462-2

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