Nonparametric Bayes local partition models for random effects.

Published

Journal Article

This paper focuses on the problem of choosing a prior for an unknown random effects distribution within a Bayesian hierarchical model. The goal is to obtain a sparse representation by allowing a combination of global and local borrowing of information. A local partition process prior is proposed, which induces dependent local clustering. Subjects can be clustered together for a subset of their parameters, and one learns about similarities between subjects increasingly as parameters are added. Some basic properties are described, including simple two-parameter expressions for marginal and conditional clustering probabilities. A slice sampler is developed which bypasses the need to approximate the countably infinite random measure in performing posterior computation. The methods are illustrated using simulation examples, and an application to hormone trajectory data.

Full Text

Duke Authors

Cited Authors

  • Dunson, DB

Published Date

  • January 2009

Published In

Volume / Issue

  • 96 / 2

Start / End Page

  • 249 - 262

PubMed ID

  • 23710074

Pubmed Central ID

  • 23710074

Electronic International Standard Serial Number (EISSN)

  • 1464-3510

International Standard Serial Number (ISSN)

  • 0006-3444

Digital Object Identifier (DOI)

  • 10.1093/biomet/asp021

Language

  • eng