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dc.contributor.authorCastillo-Carreno, Edwin
dc.contributor.authorCepeda-Cuervo, Edilberto
dc.contributor.authorNúñez-Antón, Vicente
dc.date.accessioned2022-02-10T13:56:27Z
dc.date.available2022-02-10T13:56:27Z
dc.date.issued2020-06-26
dc.identifier.citationCastillo-Carreno, E.; Cepeda-Cuervo, E.; Núñez-Antón, V. Bayesian structured antedependence model proposals for longitudinal data. "SORT", 26 Juny 2020, vol. 44, núm. 1, p. 171-200.
dc.identifier.issn1696-2281
dc.identifier.urihttp://hdl.handle.net/2117/362100
dc.description.abstractAn 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.
dc.format.extent30 p.
dc.language.isoeng
dc.publisherInstitut d'Estadística de Catalunya
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
dc.subject.otherantedependence models
dc.subject.otherBayesian methods
dc.subject.otherGibbs sampling
dc.subject.othermean-covariance modelling
dc.subject.othernonstationary correlation
dc.titleBayesian structured antedependence model proposals for longitudinal data
dc.typeArticle
dc.subject.lemacEstadística matemàtica
dc.subject.lemacEstadística matemàtica--Aplicacions
dc.identifier.doi10.2436/20.8080.02.99
dc.description.peerreviewedPeer Reviewed
dc.subject.amsClassificació AMS::62 Statistics::62F Parametric inference
dc.subject.amsClassificació AMS::62 Statistics::62J Linear inference, regression
dc.subject.amsClassificació AMS::62 Statistics::62P Applications
dc.rights.accessOpen Access
local.citation.publicationNameSORT
local.citation.volume44
local.citation.number1
local.citation.startingPage171
local.citation.endingPage200


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