Academic Journal
Bayesian structured antedependence model proposals for longitudinal data
| Τίτλος: | Bayesian structured antedependence model proposals for longitudinal data |
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| Συγγραφείς: | Castillo-Carreno, Edwin, Cepeda-Cuervo, Edilberto, Núñez-Antón, Vicente |
| Πηγή: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) Dipòsit Digital de Documents de la UAB Universitat Autònoma de Barcelona 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 Repositori Institucional de la Universitat Rovira i Virgili Universitat Rovira i virgili (URV) |
| Στοιχεία εκδότη: | Institut d'Estadística de Catalunya, 2020. |
| Έτος έκδοσης: | 2020 |
| Θεματικοί όροι: | Estadística matemàtica, Classificació AMS::62 Statistics::62J Linear inference, Bayesian methods, antedependence models, mean-covariance modelling, Estadística matemàtica--Aplicacions, Antedependence models, Classificació AMS::62 Statistics::62J Linear inference, regression, 62 Statistics::62P Applications [Classificació AMS], nonstationary correlation, Classificació AMS::62 Statistics::62F Parametric inference, Classificació AMS::62 Statistics::62P Applications, Mean-covariance modelling, Nonstationary correlation, Gibbs sampling, Matemàtiques i estadística::Estadística matemàtica [Àrees temàtiques de la UPC], Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica, 62 Statistics::62F Parametric inference [Classificació AMS], 62 Statistics::62J Linear inference, regression [Classificació AMS], regression |
| Περιγραφή: | 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. |
| Τύπος εγγράφου: | Article |
| Περιγραφή αρχείου: | application/pdf; application/zip |
| Γλώσσα: | English |
| DOI: | 10.2436/20.8080.02.99 |
| Σύνδεσμος πρόσβασης: | https://ddd.uab.cat/record/225691 http://hdl.handle.net/20.500.11797/RP4902 https://dialnet.unirioja.es/servlet/articulo?codigo=7537023 https://ddd.uab.cat/record/225691 https://ddd.uab.cat/pub/sort/sort_a2020v44n1/sort_a2020v44n1p171.pdf |
| Rights: | CC BY NC ND |
| Αριθμός Καταχώρησης: | edsair.dedup.wf.002..1930676b4b05bb715338b3945d492d00 |
| Βάση Δεδομένων: | OpenAIRE |
| DOI: | 10.2436/20.8080.02.99 |
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