Bayesian joint modelling of the mean and covariance structures for normal longitudinal data
Cepeda-Cuervo, Edilberto (Universidad Nacional de Colombia,)
Núñez-Antón, Vicente (Universidad del País Vasco)

Date: 2007
Abstract: We consider the joint modelling of the mean and covariance structures for the general antedependence model, estimating their parameters and the innovation variances in a longitudinal data context. We propose a new and computationally efficient classic estimation method based on the Fisher scoring algorithm to obtain the maximum likelihood estimates of the parameters. In addition, we also propose a new and innovative Bayesian methodology based on the Gibbs sampling, properly adapted for longitudinal data analysis, a methodology that considers linear mean structures and unrestrictedcovariance structures for normal longitudinal data. We illustrate the proposed methodology and study its strengths and weaknesses by analyzing two examples, the race and the cattle data sets.
Rights: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Language: Anglès
Document: article ; recerca ; publishedVersion
Subject: Antedependence models ; Bayes estimation ; Fisher scoring ; Gibbs sampling
Published in: SORT : statistics and operations research transactions, Vol. 31, Núm. 2 (July-December 2007) , p. 181-200, ISSN 1576-2270

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 Record created 2012-07-20, last modified 2017-10-21

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