Prediction of a multivariate spatial random field with continuous, count and ordianl outcomes
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.
| Main Authors: | , , , |
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| Format: | conference_item biblioteca |
| Language: | eng |
| Published: |
GECAMIN
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| Subjects: | U10 - Informatique, mathématiques et statistiques, modèle mathématique, http://aims.fao.org/aos/agrovoc/c_24199, |
| Online Access: | http://agritrop.cirad.fr/547647/ http://agritrop.cirad.fr/547647/1/document_547647.pdf |
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| Summary: | 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. |
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