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 about a collection of such functions. We propose a hierarchical model that allows us simultaneously to estimate multiple curves nonparametrically by using dependent Dirichlet p...
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Oxford University Press
2009
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fthighwire:oai:open-archive.highwire.org:biomet:asn054v1 2023-05-15T17:31:47+02:00 Bayesian nonparametric functional data analysis through density estimation Rodríguez, Abel Dunson, David B. Gelfand, Alan E. 2009-01-24 08:33:00.0 text/html http://biomet.oxfordjournals.org/cgi/content/short/asn054v1 https://doi.org/10.1093/biomet/asn054 en eng Oxford University Press http://biomet.oxfordjournals.org/cgi/content/short/asn054v1 http://dx.doi.org/10.1093/biomet/asn054 Copyright (C) 2009, Biometrika Trust Articles TEXT 2009 fthighwire https://doi.org/10.1093/biomet/asn054 2016-11-16T17:40:09Z In many modern experimental settings, observations are obtained in the form of functions and interest focuses on inferences about a collection of such functions. We propose a hierarchical model that allows us simultaneously to estimate multiple curves nonparametrically by using dependent Dirichlet process mixtures of Gaussian distributions 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 HighWire Press (Stanford University) Biometrika 96 1 149 162 |
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HighWire Press (Stanford University) |
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English |
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Articles Rodríguez, Abel Dunson, David B. Gelfand, Alan E. Bayesian nonparametric functional data analysis through density estimation |
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Articles |
description |
In many modern experimental settings, observations are obtained in the form of functions and interest focuses on inferences about a collection of such functions. We propose a hierarchical model that allows us simultaneously to estimate multiple curves nonparametrically by using dependent Dirichlet process mixtures of Gaussian distributions 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 |
publisher |
Oxford University Press |
publishDate |
2009 |
url |
http://biomet.oxfordjournals.org/cgi/content/short/asn054v1 https://doi.org/10.1093/biomet/asn054 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_relation |
http://biomet.oxfordjournals.org/cgi/content/short/asn054v1 http://dx.doi.org/10.1093/biomet/asn054 |
op_rights |
Copyright (C) 2009, Biometrika Trust |
op_doi |
https://doi.org/10.1093/biomet/asn054 |
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Biometrika |
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96 |
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1 |
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149 |
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162 |
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1766129554610454528 |