Papageorgiou, Georgios (2022) Bayesian semiparametric modelling of covariance matrices for multivariate longitudinal data. Statistics in Medicine 41 (14), pp. 2665-2687. ISSN 0277-6715.
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Abstract
The article develops marginal models for multivariate longitudinal responses. Overall, the model consists of five regression submodels, one for the mean and four for the covariance matrix, with the latter resulting by considering various matrix decompositions. The decompositions that we employ are intuitive, easy to understand, and they do not rely on any assumptions such as the presence of an ordering among the multivariate responses. The regression submodels are semiparametric, with unknown functions represented by basis function expansions. We use spike-slap priors for the regression coefficients to achieve variable selection and function regularization, and to obtain parameter estimates that account for model uncertainty. An efficient Markov chain Monte Carlo algorithm for posterior sampling is developed. The simulation study presented investigates the the gains that one may have when considering multivariate longitudinal analyses instead of univariate ones, and whether these gains can counteract the negative effects of missing data. We apply the methods on a highly unbalanced longitudinal dataset with four responses observed over a period of 20 years.
Metadata
| Item Type: | Article |
|---|---|
| Additional Information: | This is the peer reviewed version of the article, which has been published in final form at the link above. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. |
| Keyword(s) / Subject(s): | Cholesky decomposition, Clustering, Model averaging, Semiparametric regression, Variable selection |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Research Centres and Institutes: | Applied Macroeconomics, Birkbeck Centre for |
| Depositing User: | Georgios Papageorgiou |
| Date Deposited: | 01 Mar 2022 06:24 |
| Last Modified: | 09 Aug 2023 12:53 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/47639 |
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