Density Estimation using Quantile Variance and Quantile-Mean Covariance

Autores
Mena, Andrés Sebastián; Montes, Rojas Gabriel Victorio
Año de publicación
2018
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Based on asymptotic properties of sample Quantile Distribution derived by Hall & Martin (1988) and Ferguson (1999), we propose a novel method which explodes Quantile Variance, and Quantile-Mean Covariance to estimate distributional density from samples. The process consists in firstly estimate sample Quantile Variance and sample Quantile-Mean Covariance using bootstrap techniques and after use them to compute distributional density. We conducted Montecarlo Simulations for different Data Generating Process, sample size and parameters and we discovered that for many cases Quantile Density Estimators perform better in terms of Mean Integrated Squared Error than standard Kernel Density Estimator. Finally, we propose some smoothing techniques in order to reduce estimators variance and increase their accuracy.
Facultad de Ciencias Económicas
Materia
Ciencias Económicas
Density Estimation
Quantile Variance
Quantile-Mean Covariance
Bootstrap
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/169127
Acceder
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network_name_str SEDICI (UNLP)
spelling Density Estimation using Quantile Variance and Quantile-Mean CovarianceMena, Andrés SebastiánMontes, Rojas Gabriel VictorioCiencias EconómicasDensity EstimationQuantile VarianceQuantile-Mean CovarianceBootstrapBased on asymptotic properties of sample Quantile Distribution derived by Hall & Martin (1988) and Ferguson (1999), we propose a novel method which explodes Quantile Variance, and Quantile-Mean Covariance to estimate distributional density from samples. The process consists in firstly estimate sample Quantile Variance and sample Quantile-Mean Covariance using bootstrap techniques and after use them to compute distributional density. We conducted Montecarlo Simulations for different Data Generating Process, sample size and parameters and we discovered that for many cases Quantile Density Estimators perform better in terms of Mean Integrated Squared Error than standard Kernel Density Estimator. Finally, we propose some smoothing techniques in order to reduce estimators variance and increase their accuracy.Facultad de Ciencias Económicas2018-11info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/169127enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-28590-6-0info:eu-repo/semantics/altIdentifier/url/https://bd.aaep.org.ar/anales/works/works2018/mena_montes.pdfinfo:eu-repo/semantics/altIdentifier/issn/1852-0022info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:43:25Zoai:sedici.unlp.edu.ar:10915/169127Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:43:26.124SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Density Estimation using Quantile Variance and Quantile-Mean Covariance
title Density Estimation using Quantile Variance and Quantile-Mean Covariance
spellingShingle Density Estimation using Quantile Variance and Quantile-Mean Covariance
Mena, Andrés Sebastián
Ciencias Económicas
Density Estimation
Quantile Variance
Quantile-Mean Covariance
Bootstrap
title_short Density Estimation using Quantile Variance and Quantile-Mean Covariance
title_full Density Estimation using Quantile Variance and Quantile-Mean Covariance
title_fullStr Density Estimation using Quantile Variance and Quantile-Mean Covariance
title_full_unstemmed Density Estimation using Quantile Variance and Quantile-Mean Covariance
title_sort Density Estimation using Quantile Variance and Quantile-Mean Covariance
dc.creator.none.fl_str_mv Mena, Andrés Sebastián
Montes, Rojas Gabriel Victorio
author Mena, Andrés Sebastián
author_facet Mena, Andrés Sebastián
Montes, Rojas Gabriel Victorio
author_role author
author2 Montes, Rojas Gabriel Victorio
author2_role author
dc.subject.none.fl_str_mv Ciencias Económicas
Density Estimation
Quantile Variance
Quantile-Mean Covariance
Bootstrap
topic Ciencias Económicas
Density Estimation
Quantile Variance
Quantile-Mean Covariance
Bootstrap
dc.description.none.fl_txt_mv Based on asymptotic properties of sample Quantile Distribution derived by Hall & Martin (1988) and Ferguson (1999), we propose a novel method which explodes Quantile Variance, and Quantile-Mean Covariance to estimate distributional density from samples. The process consists in firstly estimate sample Quantile Variance and sample Quantile-Mean Covariance using bootstrap techniques and after use them to compute distributional density. We conducted Montecarlo Simulations for different Data Generating Process, sample size and parameters and we discovered that for many cases Quantile Density Estimators perform better in terms of Mean Integrated Squared Error than standard Kernel Density Estimator. Finally, we propose some smoothing techniques in order to reduce estimators variance and increase their accuracy.
Facultad de Ciencias Económicas
description Based on asymptotic properties of sample Quantile Distribution derived by Hall & Martin (1988) and Ferguson (1999), we propose a novel method which explodes Quantile Variance, and Quantile-Mean Covariance to estimate distributional density from samples. The process consists in firstly estimate sample Quantile Variance and sample Quantile-Mean Covariance using bootstrap techniques and after use them to compute distributional density. We conducted Montecarlo Simulations for different Data Generating Process, sample size and parameters and we discovered that for many cases Quantile Density Estimators perform better in terms of Mean Integrated Squared Error than standard Kernel Density Estimator. Finally, we propose some smoothing techniques in order to reduce estimators variance and increase their accuracy.
publishDate 2018
dc.date.none.fl_str_mv 2018-11
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
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http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/169127
url http://sedici.unlp.edu.ar/handle/10915/169127
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-987-28590-6-0
info:eu-repo/semantics/altIdentifier/url/https://bd.aaep.org.ar/anales/works/works2018/mena_montes.pdf
info:eu-repo/semantics/altIdentifier/issn/1852-0022
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
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reponame_str SEDICI (UNLP)
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instname_str Universidad Nacional de La Plata
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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