Revistes Publicacions URV: SORT - Statistics and Operations Research Transactions> 2020

Bayesian structured antedependence model proposals for longitudinal data

  • Identification data

    Identifier: RP:4902
  • Authors:

    Núñez-Antón, Vicente
    Cepeda-Cuervo, Edilberto
    Castillo-Carreno, Edwin
    1696-2281
    2013-8830
    SORT- Statistics and Operations Research Transactions; Vol. 44, Núm. 1 (2020): ; 171-200
    SORT-Statistics and Operations Research Transactions; Vol 44, No 1 (2020): January-June; 171-200
    oai:raco.cat:article/371188
    http://raco.cat/index.php/SORT/article/view/371188
  • Others:

    Author, as appears in the article.: Núñez-Antón, Vicente Cepeda-Cuervo, Edilberto Castillo-Carreno, Edwin
    Keywords: antedependence models
    Abstract: An important problem in Statistics is the study of longitudinal data taking into account the effect of other explanatory variables, such as treatments and time and, simultaneously, the incorporation into the model of the time dependence between observations on the same individual. The latter is specially relevant in the case of nonstationary correlations, and nonconstant variances for the different time point at which measurements are taken. Antedependence models constitute a well known commonly used set of models that can accommodate this behaviour. These covariance models can include too many parameters and estimation can be a complicated optimization problem requiring the use of complex algorithms and programming. In this paper, a new Bayesian approach to analyse longitudinal data within the context of antedependence models is proposed. This innovative approach takes into account the possibility of having nonstationary correlations and variances, and proposes a robust and computationally efficient estimation method for this type of data. We consider the joint modelling of the mean and covariance structures for the general antedependence model, estimating their parameters in a longitudinal data context. Our Bayesian approach is based on a generalization of the Gibbs sampling and Metropolis-Hastings by blocks algorithm, properly adapted to the antedependence models longitudinal data settings. Finally, we illustrate the proposed methodology by analysing several examples where antedependence models have been shown to be useful: the small mice, the speech recognition and the race data sets.
    Journal publication year: 2020
    Publication Type: ##rt.metadata.pkp.peerReviewed## info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article