The nested dirichlet process

Published

Journal Article

In multicenter studies, subjects in different centers may have different outcome distributions. This article is motivated by the problem of nonparametric modeling of these distributions, borrowing information across centers while also allowing centers to be clustered. Starting with a stick-breaking representation of the Dirichlet process (DP), we replace the random atoms with random probability measures drawn from a DP. This results in a nested DP prior, which can be placed on the collection of distributions for the different centers, with centers drawn from the same DP component automatically clustered together. Theoretical properties are discussed, and an efficient Markov chain Monte Carlo algorithm is developed for computation. The methods are illustrated using a simulation study and an application to quality of care in U.S. hospitals.

Full Text

Duke Authors

Cited Authors

  • Rodríguez, A; Dunson, DB; Gelfand, AE

Published Date

  • January 1, 2008

Published In

Volume / Issue

  • 103 / 483

Start / End Page

  • 1131 - 1154

International Standard Serial Number (ISSN)

  • 0162-1459

Digital Object Identifier (DOI)

  • 10.1198/016214508000000553

Citation Source

  • Scopus