A family of non-parametric density estimation algorithms

Autores
Tabak, E. G.; Turner, Cristina Vilma
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A new methodology for density estimation is proposed. The method- ology, which builds on the one developed in [15], normalizes the data points through the composition of simple maps. The parameters of each map are determined through the maximization of a local quadratic approximation to the log-likelihood. Various candidates for the el- ementary maps of each step are proposed; criteria for choosing one includes robustness, computational simplicity and good behavior in high-dimensional settings. A good choice is that of localized radial expansions, which depend on a single parameter: all the complex- ity of arbitrary, possibly convoluted probability densities can be built through the composition of such simple maps.
Fil: Tabak, E. G.. University Of New York; Estados Unidos
Fil: Turner, Cristina Vilma. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Centro de Investigación y Estudios de Matemática de Córdoba(p); Argentina
Materia
Density Estimation
Clustering
Non Parametric Statistic
Flux Algorithm
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/8930
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A family of non-parametric density estimation algorithmsTabak, E. G.Turner, Cristina VilmaDensity EstimationClusteringNon Parametric StatisticFlux Algorithmhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1A new methodology for density estimation is proposed. The method- ology, which builds on the one developed in [15], normalizes the data points through the composition of simple maps. The parameters of each map are determined through the maximization of a local quadratic approximation to the log-likelihood. Various candidates for the el- ementary maps of each step are proposed; criteria for choosing one includes robustness, computational simplicity and good behavior in high-dimensional settings. A good choice is that of localized radial expansions, which depend on a single parameter: all the complex- ity of arbitrary, possibly convoluted probability densities can be built through the composition of such simple maps.Fil: Tabak, E. G.. University Of New York; Estados UnidosFil: Turner, Cristina Vilma. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Centro de Investigación y Estudios de Matemática de Córdoba(p); ArgentinaWiley2013-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/8930Tabak, E. G. ; Turner, Cristina Vilma; A family of non-parametric density estimation algorithms; Wiley; Communications On Pure And Applied Mathematics; 62; 2; 2-2013; 145-1640010-3640enginfo:eu-repo/semantics/altIdentifier/doi/10.1002/cpa.21423info:eu-repo/semantics/altIdentifier/url/http://onlinelibrary.wiley.com/doi/10.1002/cpa.21423/abstractinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-11-05T09:47:54Zoai:ri.conicet.gov.ar:11336/8930instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-11-05 09:47:54.914CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A family of non-parametric density estimation algorithms
title A family of non-parametric density estimation algorithms
spellingShingle A family of non-parametric density estimation algorithms
Tabak, E. G.
Density Estimation
Clustering
Non Parametric Statistic
Flux Algorithm
title_short A family of non-parametric density estimation algorithms
title_full A family of non-parametric density estimation algorithms
title_fullStr A family of non-parametric density estimation algorithms
title_full_unstemmed A family of non-parametric density estimation algorithms
title_sort A family of non-parametric density estimation algorithms
dc.creator.none.fl_str_mv Tabak, E. G.
Turner, Cristina Vilma
author Tabak, E. G.
author_facet Tabak, E. G.
Turner, Cristina Vilma
author_role author
author2 Turner, Cristina Vilma
author2_role author
dc.subject.none.fl_str_mv Density Estimation
Clustering
Non Parametric Statistic
Flux Algorithm
topic Density Estimation
Clustering
Non Parametric Statistic
Flux Algorithm
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv A new methodology for density estimation is proposed. The method- ology, which builds on the one developed in [15], normalizes the data points through the composition of simple maps. The parameters of each map are determined through the maximization of a local quadratic approximation to the log-likelihood. Various candidates for the el- ementary maps of each step are proposed; criteria for choosing one includes robustness, computational simplicity and good behavior in high-dimensional settings. A good choice is that of localized radial expansions, which depend on a single parameter: all the complex- ity of arbitrary, possibly convoluted probability densities can be built through the composition of such simple maps.
Fil: Tabak, E. G.. University Of New York; Estados Unidos
Fil: Turner, Cristina Vilma. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Centro de Investigación y Estudios de Matemática de Córdoba(p); Argentina
description A new methodology for density estimation is proposed. The method- ology, which builds on the one developed in [15], normalizes the data points through the composition of simple maps. The parameters of each map are determined through the maximization of a local quadratic approximation to the log-likelihood. Various candidates for the el- ementary maps of each step are proposed; criteria for choosing one includes robustness, computational simplicity and good behavior in high-dimensional settings. A good choice is that of localized radial expansions, which depend on a single parameter: all the complex- ity of arbitrary, possibly convoluted probability densities can be built through the composition of such simple maps.
publishDate 2013
dc.date.none.fl_str_mv 2013-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/8930
Tabak, E. G. ; Turner, Cristina Vilma; A family of non-parametric density estimation algorithms; Wiley; Communications On Pure And Applied Mathematics; 62; 2; 2-2013; 145-164
0010-3640
url http://hdl.handle.net/11336/8930
identifier_str_mv Tabak, E. G. ; Turner, Cristina Vilma; A family of non-parametric density estimation algorithms; Wiley; Communications On Pure And Applied Mathematics; 62; 2; 2-2013; 145-164
0010-3640
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1002/cpa.21423
info:eu-repo/semantics/altIdentifier/url/http://onlinelibrary.wiley.com/doi/10.1002/cpa.21423/abstract
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.084122

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