The local Dirichlet process.

Published

Journal Article

As a generalization of the Dirichlet process (DP) to allow predictor dependence, we propose a local Dirichlet process (lDP). The lDP provides a prior distribution for a collection of random probability measures indexed by predictors. This is accomplished by assigning stick-breaking weights and atoms to random locations in a predictor space. The probability measure at a given predictor value is then formulated using the weights and atoms located in a neighborhood about that predictor value. This construction results in a marginal DP prior for the random measure at any specific predictor value. Dependence is induced through local sharing of random components. Theoretical properties are considered and a blocked Gibbs sampler is proposed for posterior computation in lDP mixture models. The methods are illustrated using simulated examples and an epidemiologic application.

Full Text

Duke Authors

Cited Authors

  • Chung, Y; Dunson, DB

Published Date

  • February 2011

Published In

Volume / Issue

  • 63 / 1

Start / End Page

  • 59 - 80

PubMed ID

  • 23645935

Pubmed Central ID

  • 23645935

Electronic International Standard Serial Number (EISSN)

  • 1572-9052

International Standard Serial Number (ISSN)

  • 0020-3157

Digital Object Identifier (DOI)

  • 10.1007/s10463-008-0218-9

Language

  • eng