A semiparametric bayesian approach to multivariate longitudinal data
Document Type
Article
Publication Title
Australian and New Zealand Journal of Statistics
Abstract
We extend the standard multivariate mixed model by incorporating a smooth time effect and relaxing distributional assumptions. We propose a semiparametric Bayesian approach to multivariate longitudinal data using a mixture of Polya trees prior distribution. Usually, the distribution of random effects in a longitudinal data model is assumed to be Gaussian. However, the normality assumption may be suspect, particularly if the estimated longitudinal trajectory parameters exhibit multi?modality and skewness. In this paper we propose a mixture of Polya trees prior density to address the limitations of the parametric random effects distribution. We illustrate the methodology by analysing data from a recent HIV?AIDS study.
Publication Date
1-4-2010
Publisher
Wiley
Volume
Vol.52
Issue
Iss.3
Recommended Citation
Ghosh, Pulak and Hanson, Timothy, "A semiparametric bayesian approach to multivariate longitudinal data" (2010). Faculty Publications. 838.
https://research.iimb.ac.in/fac_pubs/838
