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Discussion on “Competition on Spatial Statistics for Large Datasets”

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-competiti...

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Detalles Bibliográficos
Autores principales: Allard, Denis, Clarotto, Lucia, Opitz, Thomas, Romary, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8301736/
https://www.ncbi.nlm.nih.gov/pubmed/34335011
http://dx.doi.org/10.1007/s13253-021-00462-2
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author Allard, Denis
Clarotto, Lucia
Opitz, Thomas
Romary, Thomas
author_facet Allard, Denis
Clarotto, Lucia
Opitz, Thomas
Romary, Thomas
author_sort Allard, Denis
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version supplementary material available at 10.1007/s13253-021-00462-2.
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spelling pubmed-83017362021-07-26 Discussion on “Competition on Spatial Statistics for Large Datasets” Allard, Denis Clarotto, Lucia Opitz, Thomas Romary, Thomas J Agric Biol Environ Stat Article 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. SUPPLEMENTARY INFORMATION: The online version supplementary material available at 10.1007/s13253-021-00462-2. Springer US 2021-07-23 2021 /pmc/articles/PMC8301736/ /pubmed/34335011 http://dx.doi.org/10.1007/s13253-021-00462-2 Text en © International Biometric Society 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Allard, Denis
Clarotto, Lucia
Opitz, Thomas
Romary, Thomas
Discussion on “Competition on Spatial Statistics for Large Datasets”
title Discussion on “Competition on Spatial Statistics for Large Datasets”
title_full Discussion on “Competition on Spatial Statistics for Large Datasets”
title_fullStr Discussion on “Competition on Spatial Statistics for Large Datasets”
title_full_unstemmed Discussion on “Competition on Spatial Statistics for Large Datasets”
title_short Discussion on “Competition on Spatial Statistics for Large Datasets”
title_sort discussion on “competition on spatial statistics for large datasets”
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8301736/
https://www.ncbi.nlm.nih.gov/pubmed/34335011
http://dx.doi.org/10.1007/s13253-021-00462-2
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