The determination of a "least quantile of squares regression line" for all quantiles

Least median of squares regression has shown to be an extremely useful tool in robust regression analysis. In this note, we extend this concept to least quantile of squares regression, and propose a polynomial algorithm that finds simultaneously an estimator for each quantile. This leads to a propos...

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Detalles Bibliográficos
Autores: Carrizosa Priego, Emilio José, Plastria, Frank
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:1994
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/107826
Acceso en línea:https://hdl.handle.net/11441/107826
https://doi.org/10.1016/0167-9473(94)00059-R
Access Level:acceso abierto
Palabra clave:Least median of squares regression
Robust regression
Sweep-line technique
Minquantile optimization
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spelling The determination of a "least quantile of squares regression line" for all quantilesCarrizosa Priego, Emilio JoséPlastria, FrankLeast median of squares regressionRobust regressionSweep-line techniqueMinquantile optimizationLeast median of squares regression has shown to be an extremely useful tool in robust regression analysis. In this note, we extend this concept to least quantile of squares regression, and propose a polynomial algorithm that finds simultaneously an estimator for each quantile. This leads to a proposal of a robust minimum scale regression line and a polynomial algorithm for its determination.Elsevier ScienceEstadística e Investigación OperativaFQM329: Optimizacion1994info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/107826https://doi.org/10.1016/0167-9473(94)00059-Rreponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésComputational Statistics & Data Analysis, 20 (5), 467-479.https://doi.org/10.1016/0167-9473(94)00059-Rinfo:eu-repo/semantics/openAccessoai:idus.us.es:11441/1078262026-06-17T12:51:07Z
dc.title.none.fl_str_mv The determination of a "least quantile of squares regression line" for all quantiles
title The determination of a "least quantile of squares regression line" for all quantiles
spellingShingle The determination of a "least quantile of squares regression line" for all quantiles
Carrizosa Priego, Emilio José
Least median of squares regression
Robust regression
Sweep-line technique
Minquantile optimization
title_short The determination of a "least quantile of squares regression line" for all quantiles
title_full The determination of a "least quantile of squares regression line" for all quantiles
title_fullStr The determination of a "least quantile of squares regression line" for all quantiles
title_full_unstemmed The determination of a "least quantile of squares regression line" for all quantiles
title_sort The determination of a "least quantile of squares regression line" for all quantiles
dc.creator.none.fl_str_mv Carrizosa Priego, Emilio José
Plastria, Frank
author Carrizosa Priego, Emilio José
author_facet Carrizosa Priego, Emilio José
Plastria, Frank
author_role author
author2 Plastria, Frank
author2_role author
dc.contributor.none.fl_str_mv Estadística e Investigación Operativa
FQM329: Optimizacion
dc.subject.none.fl_str_mv Least median of squares regression
Robust regression
Sweep-line technique
Minquantile optimization
topic Least median of squares regression
Robust regression
Sweep-line technique
Minquantile optimization
description Least median of squares regression has shown to be an extremely useful tool in robust regression analysis. In this note, we extend this concept to least quantile of squares regression, and propose a polynomial algorithm that finds simultaneously an estimator for each quantile. This leads to a proposal of a robust minimum scale regression line and a polynomial algorithm for its determination.
publishDate 1994
dc.date.none.fl_str_mv 1994
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/107826
https://doi.org/10.1016/0167-9473(94)00059-R
url https://hdl.handle.net/11441/107826
https://doi.org/10.1016/0167-9473(94)00059-R
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Computational Statistics & Data Analysis, 20 (5), 467-479.
https://doi.org/10.1016/0167-9473(94)00059-R
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300724

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