The local Dirichlet process

Cited 33 time in webofscience Cited 0 time in scopus
  • Hit : 410
  • Download : 0
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.
Publisher
Springer Heidelberg
Issue Date
2011-02
Language
English
Article Type
Article
Citation

ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS, v.63, no.1, pp.59 - 80

ISSN
0020-3157
DOI
10.1007/s10463-008-0218-9
URI
http://hdl.handle.net/10203/93694
Appears in Collection
MA-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 33 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0