A hierarchical bayesian model for spatial prediction of multivariate non-gaussian random fields
As most georeferenced data sets are multivariate and concern variables of different types, spatial mapping methods must be able to deal with such data. The main difficulties are the prediction of non-Gaussian variables and the modeling of the dependence between processes. The aim of this article is to present a new hierarchical Bayesian approach that permits simultaneous modeling of dependent Gaussian, count, and ordinal spatial fields. This approach is based on spatial generalized linear mixed models. We use a moving average approach to model the spatial dependence between the processes. The method is first validated through a simulation study. We show that the multivariate model has better predictive abilities than the univariate one. Then the multivariate spatial hierarchical model is applied to a real data set collected in French Guiana to predict topsoil patterns.
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Subjects: | U10 - Informatique, mathématiques et statistiques, P31 - Levés et cartographie des sols, P11 - Drainage, modèle mathématique, cartographie, terre arable, sciences du sol, http://aims.fao.org/aos/agrovoc/c_24199, http://aims.fao.org/aos/agrovoc/c_1344, http://aims.fao.org/aos/agrovoc/c_18948, http://aims.fao.org/aos/agrovoc/c_7188, http://aims.fao.org/aos/agrovoc/c_3093, http://aims.fao.org/aos/agrovoc/c_3081, |
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dig-cirad-fr-5599482024-01-28T19:11:42Z http://agritrop.cirad.fr/559948/ http://agritrop.cirad.fr/559948/ A hierarchical bayesian model for spatial prediction of multivariate non-gaussian random fields. Chagneau Pierrette, Mortier Frédéric, Picard Nicolas, Braco Jean-noël. 2011. Biometrics, 67 (1) : 97-105.https://doi.org/10.1111/j.1541-0420.2010.01415.x <https://doi.org/10.1111/j.1541-0420.2010.01415.x> A hierarchical bayesian model for spatial prediction of multivariate non-gaussian random fields Chagneau, Pierrette Mortier, Frédéric Picard, Nicolas Braco, Jean-noël eng 2011 Biometrics U10 - Informatique, mathématiques et statistiques P31 - Levés et cartographie des sols P11 - Drainage modèle mathématique cartographie terre arable sciences du sol http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_18948 http://aims.fao.org/aos/agrovoc/c_7188 Guyane française France http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 As most georeferenced data sets are multivariate and concern variables of different types, spatial mapping methods must be able to deal with such data. The main difficulties are the prediction of non-Gaussian variables and the modeling of the dependence between processes. The aim of this article is to present a new hierarchical Bayesian approach that permits simultaneous modeling of dependent Gaussian, count, and ordinal spatial fields. This approach is based on spatial generalized linear mixed models. We use a moving average approach to model the spatial dependence between the processes. The method is first validated through a simulation study. We show that the multivariate model has better predictive abilities than the univariate one. Then the multivariate spatial hierarchical model is applied to a real data set collected in French Guiana to predict topsoil patterns. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/559948/1/document_559948.pdf application/pdf Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1111/j.1541-0420.2010.01415.x 10.1111/j.1541-0420.2010.01415.x http://catalogue-bibliotheques.cirad.fr/cgi-bin/koha/opac-detail.pl?biblionumber=211421 info:eu-repo/semantics/altIdentifier/doi/10.1111/j.1541-0420.2010.01415.x info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1111/j.1541-0420.2010.01415.x |
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U10 - Informatique, mathématiques et statistiques P31 - Levés et cartographie des sols P11 - Drainage modèle mathématique cartographie terre arable sciences du sol http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_18948 http://aims.fao.org/aos/agrovoc/c_7188 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 U10 - Informatique, mathématiques et statistiques P31 - Levés et cartographie des sols P11 - Drainage modèle mathématique cartographie terre arable sciences du sol http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_18948 http://aims.fao.org/aos/agrovoc/c_7188 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 |
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U10 - Informatique, mathématiques et statistiques P31 - Levés et cartographie des sols P11 - Drainage modèle mathématique cartographie terre arable sciences du sol http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_18948 http://aims.fao.org/aos/agrovoc/c_7188 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 U10 - Informatique, mathématiques et statistiques P31 - Levés et cartographie des sols P11 - Drainage modèle mathématique cartographie terre arable sciences du sol http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_18948 http://aims.fao.org/aos/agrovoc/c_7188 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 Chagneau, Pierrette Mortier, Frédéric Picard, Nicolas Braco, Jean-noël A hierarchical bayesian model for spatial prediction of multivariate non-gaussian random fields |
description |
As most georeferenced data sets are multivariate and concern variables of different types, spatial mapping methods must be able to deal with such data. The main difficulties are the prediction of non-Gaussian variables and the modeling of the dependence between processes. The aim of this article is to present a new hierarchical Bayesian approach that permits simultaneous modeling of dependent Gaussian, count, and ordinal spatial fields. This approach is based on spatial generalized linear mixed models. We use a moving average approach to model the spatial dependence between the processes. The method is first validated through a simulation study. We show that the multivariate model has better predictive abilities than the univariate one. Then the multivariate spatial hierarchical model is applied to a real data set collected in French Guiana to predict topsoil patterns. |
format |
article |
topic_facet |
U10 - Informatique, mathématiques et statistiques P31 - Levés et cartographie des sols P11 - Drainage modèle mathématique cartographie terre arable sciences du sol http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_18948 http://aims.fao.org/aos/agrovoc/c_7188 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 |
author |
Chagneau, Pierrette Mortier, Frédéric Picard, Nicolas Braco, Jean-noël |
author_facet |
Chagneau, Pierrette Mortier, Frédéric Picard, Nicolas Braco, Jean-noël |
author_sort |
Chagneau, Pierrette |
title |
A hierarchical bayesian model for spatial prediction of multivariate non-gaussian random fields |
title_short |
A hierarchical bayesian model for spatial prediction of multivariate non-gaussian random fields |
title_full |
A hierarchical bayesian model for spatial prediction of multivariate non-gaussian random fields |
title_fullStr |
A hierarchical bayesian model for spatial prediction of multivariate non-gaussian random fields |
title_full_unstemmed |
A hierarchical bayesian model for spatial prediction of multivariate non-gaussian random fields |
title_sort |
hierarchical bayesian model for spatial prediction of multivariate non-gaussian random fields |
url |
http://agritrop.cirad.fr/559948/ http://agritrop.cirad.fr/559948/1/document_559948.pdf |
work_keys_str_mv |
AT chagneaupierrette ahierarchicalbayesianmodelforspatialpredictionofmultivariatenongaussianrandomfields AT mortierfrederic ahierarchicalbayesianmodelforspatialpredictionofmultivariatenongaussianrandomfields AT picardnicolas ahierarchicalbayesianmodelforspatialpredictionofmultivariatenongaussianrandomfields AT bracojeannoel ahierarchicalbayesianmodelforspatialpredictionofmultivariatenongaussianrandomfields AT chagneaupierrette hierarchicalbayesianmodelforspatialpredictionofmultivariatenongaussianrandomfields AT mortierfrederic hierarchicalbayesianmodelforspatialpredictionofmultivariatenongaussianrandomfields AT picardnicolas hierarchicalbayesianmodelforspatialpredictionofmultivariatenongaussianrandomfields AT bracojeannoel hierarchicalbayesianmodelforspatialpredictionofmultivariatenongaussianrandomfields |
_version_ |
1792497813890793472 |