Article thumbnail

Multivariate dynamic model for ordinal outcomes

By F. Chaubert, F. MORTIER and Laurent Saint-André

Abstract

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

Topics: BIOMASS, DYNAMIC MULTIVARIATE ORDINAL PROBIT MODEL, LONGITUDINAL DATA, MCMC, ORDINAL VARIABLE, LATENT VARIABLE, REPRODUCTION GENETIQUE, QUANTIFICATION, [SDV]Life Sciences [q-bio]
Publisher: 'Elsevier BV'
Year: 2008
DOI identifier: 10.1016/j.jmva.2008.01.011
OAI identifier: oai:HAL:hal-02655698v1
Provided by: HAL-CIRAD
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • https://hal.inrae.fr/hal-02655... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.