MULTIVARIATE KERNEL PARTITION PROCESS MIXTURES.

Published

Journal Article

Mixtures provide a useful approach for relaxing parametric assumptions. Discrete mixture models induce clusters, typically with the same cluster allocation for each parameter in multivariate cases. As a more flexible approach that facilitates sparse nonparametric modeling of multivariate random effects distributions, this article proposes a kernel partition process (KPP) in which the cluster allocation varies for different parameters. The KPP is shown to be the driving measure for a multivariate ordered Chinese restaurant process that induces a highly-flexible dependence structure in local clustering. This structure allows the relative locations of the random effects to inform the clustering process, with spatially-proximal random effects likely to be assigned the same cluster index. An exact block Gibbs sampler is developed for posterior computation, avoiding truncation of the infinite measure. The methods are applied to hormone curve data, and a dependent KPP is proposed for classification from functional predictors.

Full Text

Duke Authors

Cited Authors

  • Dunson, DB

Published Date

  • October 2010

Published In

Volume / Issue

  • 20 / 4

Start / End Page

  • 1395 - 1422

PubMed ID

  • 24478563

Pubmed Central ID

  • 24478563

Electronic International Standard Serial Number (EISSN)

  • 1996-8507

International Standard Serial Number (ISSN)

  • 1017-0405

Language

  • eng