The local Dirichlet process.
Journal Article (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
- PMC3640338
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