Bayesian joint modelling of the mean and covariance structures for normal longitudinal data
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 al...
| Autores: | , |
|---|---|
| Tipo de documento: | artigo |
| Data de publicação: | 2007 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositório: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglês |
| OAI Identifier: | oai:upcommons.upc.edu:2099/8917 |
| Acesso em linha: | https://hdl.handle.net/2099/8917 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Mathematical statistics Antedependence models Bayes estimation Fisher scoring Gibbs sampling Fisher scoring Estadística matemàtica Estadística matemàtica -- Aplicacions Classificació AMS::62 Statistics::62F Parametric inference Classificació AMS::62 Statistics::62P Applications Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística aplicada |
| Resumo: | 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 unrestricted covariance 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. |
|---|