Bayesian Nonparametric Functional Data Analysis Through Density Estimation.

Published

Journal Article

In many modern experimental settings, observations are obtained in the form of functions, and interest focuses on inferences on a collection of such functions. We propose a hierarchical model that allows us to simultaneously estimate multiple curves nonparametrically by using dependent Dirichlet Process mixtures of Gaussians to characterize the joint distribution of predictors and outcomes. Function estimates are then induced through the conditional distribution of the outcome given the predictors. The resulting approach allows for flexible estimation and clustering, while borrowing information across curves. We also show that the function estimates we obtain are consistent on the space of integrable functions. As an illustration, we consider an application to the analysis of Conductivity and Temperature at Depth data in the north Atlantic.

Full Text

Duke Authors

Cited Authors

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

Published Date

  • January 2009

Published In

Volume / Issue

  • 96 / 1

Start / End Page

  • 149 - 162

PubMed ID

  • 19262739

Pubmed Central ID

  • 19262739

Electronic International Standard Serial Number (EISSN)

  • 1464-3510

International Standard Serial Number (ISSN)

  • 0006-3444

Digital Object Identifier (DOI)

  • 10.1093/biomet/asn054

Language

  • eng