Hierarchical Bayesian model for Gaussian, poisson and ordinal random fields
As most georeferenced data sets are multivariate and concern variables of different kinds, spatial mapping methods must be able to deal with such data. The main difficulties are the prediction of non Gaussian variables and the modelling of the dependence between processes. The aim of this paper is to propose a new approach that permits simultaneous modelling of Gaussian, count and ordinal spatial processes. We consider a hierarchical model implemented within a Bayesian framework. The method used for Gaussian and count variables is based on the generalized linear mixed models. Ordinal variable is taken into account through a generalization of the ordinal probit model. We use a moving average approach to model the spatial dependence between the processes. The proposed model is applied to pedological data collected in French Guiana.
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Subjects: | U10 - Informatique, mathématiques et statistiques, P11 - Drainage, P31 - Levés et cartographie des sols, sciences du sol, modèle mathématique, http://aims.fao.org/aos/agrovoc/c_7188, http://aims.fao.org/aos/agrovoc/c_24199, http://aims.fao.org/aos/agrovoc/c_3093, http://aims.fao.org/aos/agrovoc/c_3081, |
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dig-cirad-fr-5545802024-01-28T18:19:56Z http://agritrop.cirad.fr/554580/ http://agritrop.cirad.fr/554580/ Hierarchical Bayesian model for Gaussian, poisson and ordinal random fields. Chagneau Pierrette, Mortier Frédéric, Picard Nicolas, Bacro Jean-Noël. 2010. In : GeoENV VII - geostatistics for environmental applications : proceedings of the seventh European conference on geostatistics for environmental applications. Atkinson Peter M. (ed.), Lloyd Christopher D. (ed.). Dordrecht : Springer [Pays-Bas], 333-344. (Quantitative geology and geostatistics, 16) ISBN 978-90-481-2321-6 European conference on geostatistics for environmental applications. 7, Southampton, Royaume-Uni, Septembre 2008.https://doi.org/10.1007/978-90-481-2322-3_29 <https://doi.org/10.1007/978-90-481-2322-3_29> Hierarchical Bayesian model for Gaussian, poisson and ordinal random fields Chagneau, Pierrette Mortier, Frédéric Picard, Nicolas Bacro, Jean-Noël eng 2010 Springer [Pays-Bas] GeoENV VII - geostatistics for environmental applications : proceedings of the seventh European conference on geostatistics for environmental applications U10 - Informatique, mathématiques et statistiques P11 - Drainage P31 - Levés et cartographie des sols sciences du sol modèle mathématique http://aims.fao.org/aos/agrovoc/c_7188 http://aims.fao.org/aos/agrovoc/c_24199 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 kinds, spatial mapping methods must be able to deal with such data. The main difficulties are the prediction of non Gaussian variables and the modelling of the dependence between processes. The aim of this paper is to propose a new approach that permits simultaneous modelling of Gaussian, count and ordinal spatial processes. We consider a hierarchical model implemented within a Bayesian framework. The method used for Gaussian and count variables is based on the generalized linear mixed models. Ordinal variable is taken into account through a generalization of the ordinal probit model. We use a moving average approach to model the spatial dependence between the processes. The proposed model is applied to pedological data collected in French Guiana. conference_item info:eu-repo/semantics/conferenceObject Conference info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/554580/1/document_554580.pdf application/pdf Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1007/978-90-481-2322-3_29 10.1007/978-90-481-2322-3_29 http://catalogue-bibliotheques.cirad.fr/cgi-bin/koha/opac-detail.pl?biblionumber=207094 info:eu-repo/semantics/altIdentifier/doi/10.1007/978-90-481-2322-3_29 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1007/978-90-481-2322-3_29 |
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U10 - Informatique, mathématiques et statistiques P11 - Drainage P31 - Levés et cartographie des sols sciences du sol modèle mathématique http://aims.fao.org/aos/agrovoc/c_7188 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 U10 - Informatique, mathématiques et statistiques P11 - Drainage P31 - Levés et cartographie des sols sciences du sol modèle mathématique http://aims.fao.org/aos/agrovoc/c_7188 http://aims.fao.org/aos/agrovoc/c_24199 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 P11 - Drainage P31 - Levés et cartographie des sols sciences du sol modèle mathématique http://aims.fao.org/aos/agrovoc/c_7188 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 U10 - Informatique, mathématiques et statistiques P11 - Drainage P31 - Levés et cartographie des sols sciences du sol modèle mathématique http://aims.fao.org/aos/agrovoc/c_7188 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 Chagneau, Pierrette Mortier, Frédéric Picard, Nicolas Bacro, Jean-Noël Hierarchical Bayesian model for Gaussian, poisson and ordinal random fields |
description |
As most georeferenced data sets are multivariate and concern variables of different kinds, spatial mapping methods must be able to deal with such data. The main difficulties are the prediction of non Gaussian variables and the modelling of the dependence between processes. The aim of this paper is to propose a new approach that permits simultaneous modelling of Gaussian, count and ordinal spatial processes. We consider a hierarchical model implemented within a Bayesian framework. The method used for Gaussian and count variables is based on the generalized linear mixed models. Ordinal variable is taken into account through a generalization of the ordinal probit model. We use a moving average approach to model the spatial dependence between the processes. The proposed model is applied to pedological data collected in French Guiana. |
format |
conference_item |
topic_facet |
U10 - Informatique, mathématiques et statistiques P11 - Drainage P31 - Levés et cartographie des sols sciences du sol modèle mathématique http://aims.fao.org/aos/agrovoc/c_7188 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 |
author |
Chagneau, Pierrette Mortier, Frédéric Picard, Nicolas Bacro, Jean-Noël |
author_facet |
Chagneau, Pierrette Mortier, Frédéric Picard, Nicolas Bacro, Jean-Noël |
author_sort |
Chagneau, Pierrette |
title |
Hierarchical Bayesian model for Gaussian, poisson and ordinal random fields |
title_short |
Hierarchical Bayesian model for Gaussian, poisson and ordinal random fields |
title_full |
Hierarchical Bayesian model for Gaussian, poisson and ordinal random fields |
title_fullStr |
Hierarchical Bayesian model for Gaussian, poisson and ordinal random fields |
title_full_unstemmed |
Hierarchical Bayesian model for Gaussian, poisson and ordinal random fields |
title_sort |
hierarchical bayesian model for gaussian, poisson and ordinal random fields |
publisher |
Springer [Pays-Bas] |
url |
http://agritrop.cirad.fr/554580/ http://agritrop.cirad.fr/554580/1/document_554580.pdf |
work_keys_str_mv |
AT chagneaupierrette hierarchicalbayesianmodelforgaussianpoissonandordinalrandomfields AT mortierfrederic hierarchicalbayesianmodelforgaussianpoissonandordinalrandomfields AT picardnicolas hierarchicalbayesianmodelforgaussianpoissonandordinalrandomfields AT bacrojeannoel hierarchicalbayesianmodelforgaussianpoissonandordinalrandomfields |
_version_ |
1792497562648838144 |