A comparison of computational approaches for maximum likelihood estimation of the Dirichlet parameters on high-dimensional data

Likelihood estimates of the Dirichlet distribution parameters can be obtained only through numerical algorithms. Such algorithms can provide estimates outside the correct range for the parameters and/or can require a large amount of iterations to reach convergence. These problems can be aggravated i...

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Detalles Bibliográficos
Autores: Giordan, Marco, Wehrens, Ron
Tipo de recurso: artículo
Fecha de publicación:2015
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/88522
Acceso en línea:https://hdl.handle.net/2117/88522
Access Level:acceso abierto
Palabra clave:Levenberg-Marquardt algorithm
re-parametrization
starting values
metabolomics data
Classificació AMS::65 Numerical analysis::65C Probabilistic methods, simulation and stochastic differential equations
Classificació AMS::62 Statistics::62F Parametric inference
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
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spelling A comparison of computational approaches for maximum likelihood estimation of the Dirichlet parameters on high-dimensional dataGiordan, MarcoWehrens, RonLevenberg-Marquardt algorithmre-parametrizationstarting valuesmetabolomics dataClassificació AMS::65 Numerical analysis::65C Probabilistic methods, simulation and stochastic differential equationsClassificació AMS::62 Statistics::62F Parametric inferenceÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàticaLikelihood estimates of the Dirichlet distribution parameters can be obtained only through numerical algorithms. Such algorithms can provide estimates outside the correct range for the parameters and/or can require a large amount of iterations to reach convergence. These problems can be aggravated if good starting values are not provided. In this paper we discuss several approaches that can partially avoid these problems providing a good trade-off between efficiency and stability. The performances of these approaches are compared on high-dimensional real and simulated data.Peer ReviewedInstitut d'Estadística de Catalunya20152015-06-0120162016-07-05journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/88522reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/885222026-05-27T15:37:01Z
dc.title.none.fl_str_mv A comparison of computational approaches for maximum likelihood estimation of the Dirichlet parameters on high-dimensional data
title A comparison of computational approaches for maximum likelihood estimation of the Dirichlet parameters on high-dimensional data
spellingShingle A comparison of computational approaches for maximum likelihood estimation of the Dirichlet parameters on high-dimensional data
Giordan, Marco
Levenberg-Marquardt algorithm
re-parametrization
starting values
metabolomics data
Classificació AMS::65 Numerical analysis::65C Probabilistic methods, simulation and stochastic differential equations
Classificació AMS::62 Statistics::62F Parametric inference
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
title_short A comparison of computational approaches for maximum likelihood estimation of the Dirichlet parameters on high-dimensional data
title_full A comparison of computational approaches for maximum likelihood estimation of the Dirichlet parameters on high-dimensional data
title_fullStr A comparison of computational approaches for maximum likelihood estimation of the Dirichlet parameters on high-dimensional data
title_full_unstemmed A comparison of computational approaches for maximum likelihood estimation of the Dirichlet parameters on high-dimensional data
title_sort A comparison of computational approaches for maximum likelihood estimation of the Dirichlet parameters on high-dimensional data
dc.creator.none.fl_str_mv Giordan, Marco
Wehrens, Ron
author Giordan, Marco
author_facet Giordan, Marco
Wehrens, Ron
author_role author
author2 Wehrens, Ron
author2_role author
dc.subject.none.fl_str_mv Levenberg-Marquardt algorithm
re-parametrization
starting values
metabolomics data
Classificació AMS::65 Numerical analysis::65C Probabilistic methods, simulation and stochastic differential equations
Classificació AMS::62 Statistics::62F Parametric inference
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
topic Levenberg-Marquardt algorithm
re-parametrization
starting values
metabolomics data
Classificació AMS::65 Numerical analysis::65C Probabilistic methods, simulation and stochastic differential equations
Classificació AMS::62 Statistics::62F Parametric inference
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
description Likelihood estimates of the Dirichlet distribution parameters can be obtained only through numerical algorithms. Such algorithms can provide estimates outside the correct range for the parameters and/or can require a large amount of iterations to reach convergence. These problems can be aggravated if good starting values are not provided. In this paper we discuss several approaches that can partially avoid these problems providing a good trade-off between efficiency and stability. The performances of these approaches are compared on high-dimensional real and simulated data.
publishDate 2015
dc.date.none.fl_str_mv 2015
2015-06-01
2016
2016-07-05
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/88522
url https://hdl.handle.net/2117/88522
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Institut d'Estadística de Catalunya
publisher.none.fl_str_mv Institut d'Estadística de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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