Publicação: Bayesian longitudinal data analysis with mixed models and thick-tailed distributions using MCMC
Carregando...
Data
Autores
Orientador
Coorientador
Pós-graduação
Curso de graduação
Título da Revista
ISSN da Revista
Título de Volume
Editor
Carfax Publishing
Tipo
Artigo
Direito de acesso
Acesso restrito
Resumo
Linear 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.
Descrição
Palavras-chave
robust-inference, longitudinal study, mixed model, thick-tailed distribution, heteroscedasticity, Bayesian inference
Idioma
Inglês
Como citar
Journal of Applied Statistics. Basingstoke: Carfax Publishing, v. 31, n. 7, p. 855-873, 2004.
