Multivariate dynamic model for ordinal outcomes
Individual or stand-level biomass is not easy to measure. The current methods employed, based on cutting down a representative sample of plantations, make it possible to assess the biomasses for various compartments (bark, dead branches, leaves, ¿). However, this felling makes individual longitudinal follow-up impossible. In this context, we propose a method to evaluate individual biomasses by compartments when these are ordinals. Biomass is measured visually and observations are therefore not destructive. The technique is based on a probit model redefined in terms of latent variables. A generalization of the univariate case to the multivariate case is then natural and takes into account of dependency between compartment biomasses. These models are then extended to the longitudinal case by developing a Dynamic Multivariate Ordinal Probit Model. The performance of the MCMC algorithm used for the estimation is illustrated by means of simulations built from known biomass models. The quality of the estimates and the impact of certain parameters, are then discussed.
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Subjects: | U10 - Informatique, mathématiques et statistiques, biomasse, modèle mathématique, modèle dynamique, mesure (activité), métrologie, http://aims.fao.org/aos/agrovoc/c_926, http://aims.fao.org/aos/agrovoc/c_24199, http://aims.fao.org/aos/agrovoc/c_28670, http://aims.fao.org/aos/agrovoc/c_4668, http://aims.fao.org/aos/agrovoc/c_6a3cd403, |
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dig-cirad-fr-5458212024-01-28T16:14:22Z http://agritrop.cirad.fr/545821/ http://agritrop.cirad.fr/545821/ Multivariate dynamic model for ordinal outcomes. Chaubert Florence, Mortier Frédéric, Saint André Laurent. 2008. Journal of Multivariate Analysis, 99 (8) : 1717-1732.https://doi.org/10.1016/j.jmva.2008.01.011 <https://doi.org/10.1016/j.jmva.2008.01.011> Multivariate dynamic model for ordinal outcomes Chaubert, Florence Mortier, Frédéric Saint André, Laurent eng 2008 Journal of Multivariate Analysis U10 - Informatique, mathématiques et statistiques biomasse modèle mathématique modèle dynamique mesure (activité) métrologie http://aims.fao.org/aos/agrovoc/c_926 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_28670 http://aims.fao.org/aos/agrovoc/c_4668 http://aims.fao.org/aos/agrovoc/c_6a3cd403 Individual or stand-level biomass is not easy to measure. The current methods employed, based on cutting down a representative sample of plantations, make it possible to assess the biomasses for various compartments (bark, dead branches, leaves, ¿). However, this felling makes individual longitudinal follow-up impossible. In this context, we propose a method to evaluate individual biomasses by compartments when these are ordinals. Biomass is measured visually and observations are therefore not destructive. The technique is based on a probit model redefined in terms of latent variables. A generalization of the univariate case to the multivariate case is then natural and takes into account of dependency between compartment biomasses. These models are then extended to the longitudinal case by developing a Dynamic Multivariate Ordinal Probit Model. The performance of the MCMC algorithm used for the estimation is illustrated by means of simulations built from known biomass models. The quality of the estimates and the impact of certain parameters, are then discussed. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/545821/1/document_545821.pdf application/pdf Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1016/j.jmva.2008.01.011 10.1016/j.jmva.2008.01.011 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jmva.2008.01.011 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1016/j.jmva.2008.01.011 |
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U10 - Informatique, mathématiques et statistiques biomasse modèle mathématique modèle dynamique mesure (activité) métrologie http://aims.fao.org/aos/agrovoc/c_926 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_28670 http://aims.fao.org/aos/agrovoc/c_4668 http://aims.fao.org/aos/agrovoc/c_6a3cd403 U10 - Informatique, mathématiques et statistiques biomasse modèle mathématique modèle dynamique mesure (activité) métrologie http://aims.fao.org/aos/agrovoc/c_926 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_28670 http://aims.fao.org/aos/agrovoc/c_4668 http://aims.fao.org/aos/agrovoc/c_6a3cd403 |
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U10 - Informatique, mathématiques et statistiques biomasse modèle mathématique modèle dynamique mesure (activité) métrologie http://aims.fao.org/aos/agrovoc/c_926 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_28670 http://aims.fao.org/aos/agrovoc/c_4668 http://aims.fao.org/aos/agrovoc/c_6a3cd403 U10 - Informatique, mathématiques et statistiques biomasse modèle mathématique modèle dynamique mesure (activité) métrologie http://aims.fao.org/aos/agrovoc/c_926 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_28670 http://aims.fao.org/aos/agrovoc/c_4668 http://aims.fao.org/aos/agrovoc/c_6a3cd403 Chaubert, Florence Mortier, Frédéric Saint André, Laurent Multivariate dynamic model for ordinal outcomes |
description |
Individual or stand-level biomass is not easy to measure. The current methods employed, based on cutting down a representative sample of plantations, make it possible to assess the biomasses for various compartments (bark, dead branches, leaves, ¿). However, this felling makes individual longitudinal follow-up impossible. In this context, we propose a method to evaluate individual biomasses by compartments when these are ordinals. Biomass is measured visually and observations are therefore not destructive. The technique is based on a probit model redefined in terms of latent variables. A generalization of the univariate case to the multivariate case is then natural and takes into account of dependency between compartment biomasses. These models are then extended to the longitudinal case by developing a Dynamic Multivariate Ordinal Probit Model. The performance of the MCMC algorithm used for the estimation is illustrated by means of simulations built from known biomass models. The quality of the estimates and the impact of certain parameters, are then discussed. |
format |
article |
topic_facet |
U10 - Informatique, mathématiques et statistiques biomasse modèle mathématique modèle dynamique mesure (activité) métrologie http://aims.fao.org/aos/agrovoc/c_926 http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_28670 http://aims.fao.org/aos/agrovoc/c_4668 http://aims.fao.org/aos/agrovoc/c_6a3cd403 |
author |
Chaubert, Florence Mortier, Frédéric Saint André, Laurent |
author_facet |
Chaubert, Florence Mortier, Frédéric Saint André, Laurent |
author_sort |
Chaubert, Florence |
title |
Multivariate dynamic model for ordinal outcomes |
title_short |
Multivariate dynamic model for ordinal outcomes |
title_full |
Multivariate dynamic model for ordinal outcomes |
title_fullStr |
Multivariate dynamic model for ordinal outcomes |
title_full_unstemmed |
Multivariate dynamic model for ordinal outcomes |
title_sort |
multivariate dynamic model for ordinal outcomes |
url |
http://agritrop.cirad.fr/545821/ http://agritrop.cirad.fr/545821/1/document_545821.pdf |
work_keys_str_mv |
AT chaubertflorence multivariatedynamicmodelforordinaloutcomes AT mortierfrederic multivariatedynamicmodelforordinaloutcomes AT saintandrelaurent multivariatedynamicmodelforordinaloutcomes |
_version_ |
1792496965952471040 |