Prediction of a multivariate spatial random field with continuous, count and ordianl outcomes

Chagneau Pierrette, Mortier Frédéric, Picard Nicolas, Bacro Jean-Noël. 2008. Prediction of a multivariate spatial random field with continuous, count and ordianl outcomes. In : GEOSTATS 2008 : 8th International Geostatistics Congress, Santiago, Chili, 1-5 December 2008. Eds by Julián M. Ortiz, Xavier Emery. Santiago : GECAMIN, 479-488. International Geostatistics Congress. 8, Santiago, Chili, 1 Décembre 2008/5 Décembre 2008.

Résumé : 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 dependence modelling between processes. The aim of this paper is to present 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 model. Ordinal variable is taken into account through a generalization of the ordinal probit model. We use the moving average approach of Ver Hoef and Barry to model the dependencies between the processes.

Mots-clés Agrovoc : modèle mathématique

Classification Agris : U10 - Informatique, mathématiques et statistiques

Auteurs et affiliations

• Chagneau Pierrette, CIRAD-ES-UPR Dynamique forestière (FRA)
• Mortier Frédéric, CIRAD-BIOS-UPR Génétique forestière (FRA)
• Picard Nicolas, CIRAD-ES-UPR Dynamique forestière (GAB)
• Bacro Jean-Noël

Source : Cirad - Agritrop (https://agritrop.cirad.fr/547647/)

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