Bayesian inference on multivariate-<i>t</i> nonlinear mixed-effects models for multiple longitudinal data with missing values

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dc.contributor.authorWang, Wan-Lun
dc.contributor.authorCastro, Luis M.
dc.date.accessioned2025-01-23T21:23:00Z
dc.date.available2025-01-23T21:23:00Z
dc.date.issued2018
dc.description.abstractThe multivariate-t nonlinear mixed-effects model (MtNLMM) has been shown to be a promising robust tool for analyzing multiple longitudinal trajectories following arbitrary growth patterns in the presence of outliers and possible missing responses. Owing to intractable likelihood function of the model, we devise a fully Bayesian estimating procedure to account for the uncertainties of model parameters, random effects, and missing responses via the Markov chain Monte Carlo method. Posterior predictive inferences for the future values and missing responses are also investigated. We conduct a simulation study to demonstrate the feasibility of our Bayesian sampling schemes. The proposed techniques are illustrated through applications to two case studies.
dc.fuente.origenWOS
dc.identifier.eissn1938-7997
dc.identifier.issn1938-7989
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/101270
dc.identifier.wosidWOS:000426874600005
dc.issue.numero2
dc.language.isoen
dc.pagina.final264
dc.pagina.inicio251
dc.revistaStatistics and its interface
dc.rightsacceso restringido
dc.subjectMissing responses
dc.subjectMultivariate longitudinal data
dc.subjectNonlinear mean profiles
dc.subjectPosterior distributions
dc.subjectTaylor series expansion
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleBayesian inference on multivariate-<i>t</i> nonlinear mixed-effects models for multiple longitudinal data with missing values
dc.typeartículo
dc.volumen11
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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