Bayesian Nonparametric Functional Data Analysis Through Density Estimation
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 Pro...
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ftpubmed:oai:pubmedcentral.nih.gov:2650433 2023-05-15T17:31:44+02:00 Bayesian Nonparametric Functional Data Analysis Through Density Estimation Rodríguez, Abel Dunson, David B. Gelfand, Alan E. 2009 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2650433 http://www.ncbi.nlm.nih.gov/pubmed/19262739 en eng http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2650433 http://www.ncbi.nlm.nih.gov/pubmed/19262739 Article Text 2009 ftpubmed 2013-09-02T11:13:27Z 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. Text North Atlantic PubMed Central (PMC) |
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Article Rodríguez, Abel Dunson, David B. Gelfand, Alan E. Bayesian Nonparametric Functional Data Analysis Through Density Estimation |
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description |
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. |
format |
Text |
author |
Rodríguez, Abel Dunson, David B. Gelfand, Alan E. |
author_facet |
Rodríguez, Abel Dunson, David B. Gelfand, Alan E. |
author_sort |
Rodríguez, Abel |
title |
Bayesian Nonparametric Functional Data Analysis Through Density Estimation |
title_short |
Bayesian Nonparametric Functional Data Analysis Through Density Estimation |
title_full |
Bayesian Nonparametric Functional Data Analysis Through Density Estimation |
title_fullStr |
Bayesian Nonparametric Functional Data Analysis Through Density Estimation |
title_full_unstemmed |
Bayesian Nonparametric Functional Data Analysis Through Density Estimation |
title_sort |
bayesian nonparametric functional data analysis through density estimation |
publishDate |
2009 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2650433 http://www.ncbi.nlm.nih.gov/pubmed/19262739 |
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North Atlantic |
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North Atlantic |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2650433 http://www.ncbi.nlm.nih.gov/pubmed/19262739 |
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1766129462833840128 |