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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ftrepec:oai:RePEc:oup:biomet:v:96:y:2009:i:1:p:149-162 2024-04-14T08:15:44+00:00 Bayesian nonparametric functional data analysis through density estimation Abel Rodríguez David B. Dunson Alan E. Gelfand http://hdl.handle.net/10.1093/biomet/asn054 unknown http://hdl.handle.net/10.1093/biomet/asn054 article ftrepec 2024-03-19T10:32:00Z 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. Copyright 2009, Oxford University Press. Article in Journal/Newspaper North Atlantic RePEc (Research Papers in Economics) |
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RePEc (Research Papers in Economics) |
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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. Copyright 2009, Oxford University Press. |
format |
Article in Journal/Newspaper |
author |
Abel Rodríguez David B. Dunson Alan E. Gelfand |
spellingShingle |
Abel Rodríguez David B. Dunson Alan E. Gelfand Bayesian nonparametric functional data analysis through density estimation |
author_facet |
Abel Rodríguez David B. Dunson Alan E. Gelfand |
author_sort |
Abel Rodríguez |
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 |
url |
http://hdl.handle.net/10.1093/biomet/asn054 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_relation |
http://hdl.handle.net/10.1093/biomet/asn054 |
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1796314158635941888 |