Bayesian nonparametric hierarchical modeling.

Published

Journal Article

In biomedical research, hierarchical models are very widely used to accommodate dependence in multivariate and longitudinal data and for borrowing of information across data from different sources. A primary concern in hierarchical modeling is sensitivity to parametric assumptions, such as linearity and normality of the random effects. Parametric assumptions on latent variable distributions can be challenging to check and are typically unwarranted, given available prior knowledge. This article reviews some recent developments in Bayesian nonparametric methods motivated by complex, multivariate and functional data collected in biomedical studies. The author provides a brief review of flexible parametric approaches relying on finite mixtures and latent class modeling. Dirichlet process mixture models are motivated by the need to generalize these approaches to avoid assuming a fixed finite number of classes. Focusing on an epidemiology application, the author illustrates the practical utility and potential of nonparametric Bayes methods.

Full Text

Duke Authors

Cited Authors

  • Dunson, DB

Published Date

  • April 2009

Published In

Volume / Issue

  • 51 / 2

Start / End Page

  • 273 - 284

PubMed ID

  • 19358217

Pubmed Central ID

  • 19358217

Electronic International Standard Serial Number (EISSN)

  • 1521-4036

International Standard Serial Number (ISSN)

  • 0323-3847

Digital Object Identifier (DOI)

  • 10.1002/bimj.200800183

Language

  • eng