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dc.contributor.authorRosa, GJM-
dc.contributor.authorGianola, D.-
dc.contributor.authorPadovani, C. R.-
dc.identifier.citationJournal of Applied Statistics. Basingstoke: Carfax Publishing, v. 31, n. 7, p. 855-873, 2004.-
dc.description.abstractLinear mixed effects models are frequently used to analyse longitudinal data, due to their flexibility in modelling the covariance structure between and within observations. Further, it is easy to deal with unbalanced data, either with respect to the number of observations per subject or per time period, and with varying time intervals between observations. In most applications of mixed models to biological sciences, a normal distribution is assumed both for the random effects and for the residuals. This, however, makes inferences vulnerable to the presence of outliers. Here, linear mixed models employing thick-tailed distributions for robust inferences in longitudinal data analysis are described. Specific distributions discussed include the Student-t, the slash and the contaminated normal. A Bayesian framework is adopted, and the Gibbs sampler and the Metropolis-Hastings algorithms are used to carry out the posterior analyses. An example with data on orthodontic distance growth in children is discussed to illustrate the methodology. Analyses based on either the Student-t distribution or on the usual Gaussian assumption are contrasted. The thick-tailed distributions provide an appealing robust alternative to the Gaussian process for modelling distributions of the random effects and of residuals in linear mixed models, and the MCMC implementation allows the computations to be performed in a flexible manner.en
dc.publisherCarfax Publishing-
dc.sourceWeb of Science-
dc.subjectlongitudinal studypt
dc.subjectmixed modelpt
dc.subjectthick-tailed distributionpt
dc.subjectBayesian inferencept
dc.titleBayesian longitudinal data analysis with mixed models and thick-tailed distributions using MCMCen
dc.contributor.institutionMichigan State University-
dc.contributor.institutionUniv Wisconsin-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationMichigan State Univ, Dept Anim Sci, E Lansing, MI 48824 USA-
dc.description.affiliationUniv Wisconsin, Madison, WI USA-
dc.description.affiliationUNESP, São Paulo, Brazil-
dc.description.affiliationUnespUNESP, São Paulo, Brazil-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofJournal of Applied Statistics-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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