Influence diagnostics in exponentiated-Weibull regression models with censored data.

Diagnostic methods have been an important tool in regression analysis to detect anomalies, such as departures from the error assumptions and the presence of outliers and influential observations with the fitted models. The literature provides plenty of approaches for detecting outlying or influentia...

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Detalhes bibliográficos
Autores: Ortega, Edwin M. M., Cancho, Vicente G., Bolfarine, Heleno
Tipo de documento: artigo
Data de publicação:2006
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositório:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglês
OAI Identifier:oai:upcommons.upc.edu:2099/3793
Acesso em linha:https://hdl.handle.net/2099/3793
Access Level:Acceso aberto
Palavra-chave:Multivariate analysis
Inference
Survival Analysis
Anàlisi multivariable
Inferència
Estadística
Classificació AMS::62 Statistics::62H Multivariate analysis
Classificació AMS::62 Statistics::62J Linear inference, regression
Classificació AMS::62 Statistics::62N Survival analysis and censored data
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spelling Influence diagnostics in exponentiated-Weibull regression models with censored data.Ortega, Edwin M. M.Cancho, Vicente G.Bolfarine, HelenoMultivariate analysisInferenceSurvival AnalysisAnàlisi multivariableInferènciaEstadísticaClassificació AMS::62 Statistics::62H Multivariate analysisClassificació AMS::62 Statistics::62J Linear inference, regressionClassificació AMS::62 Statistics::62N Survival analysis and censored dataDiagnostic methods have been an important tool in regression analysis to detect anomalies, such as departures from the error assumptions and the presence of outliers and influential observations with the fitted models. The literature provides plenty of approaches for detecting outlying or influential observations in data sets. In this paper, we follow the local influence approach (Cook 1986) in detecting influential observations with exponentiated-Weibull regression models. The relevance of the approach is illustrated with a real data set, where it is shown that by removing the most influential observations, there is a change in the decision about which model fits the data better.Peer ReviewedInstitut d'Estadística de Catalunya20062006-01-0120072007-11-15journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2099/3793reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 2.5 Spainhttp://creativecommons.org/licenses/by-nc-nd/2.5/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2099/37932026-05-27T15:37:01Z
dc.title.none.fl_str_mv Influence diagnostics in exponentiated-Weibull regression models with censored data.
title Influence diagnostics in exponentiated-Weibull regression models with censored data.
spellingShingle Influence diagnostics in exponentiated-Weibull regression models with censored data.
Ortega, Edwin M. M.
Multivariate analysis
Inference
Survival Analysis
Anàlisi multivariable
Inferència
Estadística
Classificació AMS::62 Statistics::62H Multivariate analysis
Classificació AMS::62 Statistics::62J Linear inference, regression
Classificació AMS::62 Statistics::62N Survival analysis and censored data
title_short Influence diagnostics in exponentiated-Weibull regression models with censored data.
title_full Influence diagnostics in exponentiated-Weibull regression models with censored data.
title_fullStr Influence diagnostics in exponentiated-Weibull regression models with censored data.
title_full_unstemmed Influence diagnostics in exponentiated-Weibull regression models with censored data.
title_sort Influence diagnostics in exponentiated-Weibull regression models with censored data.
dc.creator.none.fl_str_mv Ortega, Edwin M. M.
Cancho, Vicente G.
Bolfarine, Heleno
author Ortega, Edwin M. M.
author_facet Ortega, Edwin M. M.
Cancho, Vicente G.
Bolfarine, Heleno
author_role author
author2 Cancho, Vicente G.
Bolfarine, Heleno
author2_role author
author
dc.subject.none.fl_str_mv Multivariate analysis
Inference
Survival Analysis
Anàlisi multivariable
Inferència
Estadística
Classificació AMS::62 Statistics::62H Multivariate analysis
Classificació AMS::62 Statistics::62J Linear inference, regression
Classificació AMS::62 Statistics::62N Survival analysis and censored data
topic Multivariate analysis
Inference
Survival Analysis
Anàlisi multivariable
Inferència
Estadística
Classificació AMS::62 Statistics::62H Multivariate analysis
Classificació AMS::62 Statistics::62J Linear inference, regression
Classificació AMS::62 Statistics::62N Survival analysis and censored data
description Diagnostic methods have been an important tool in regression analysis to detect anomalies, such as departures from the error assumptions and the presence of outliers and influential observations with the fitted models. The literature provides plenty of approaches for detecting outlying or influential observations in data sets. In this paper, we follow the local influence approach (Cook 1986) in detecting influential observations with exponentiated-Weibull regression models. The relevance of the approach is illustrated with a real data set, where it is shown that by removing the most influential observations, there is a change in the decision about which model fits the data better.
publishDate 2006
dc.date.none.fl_str_mv 2006
2006-01-01
2007
2007-11-15
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/2099/3793
url https://hdl.handle.net/2099/3793
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 2.5 Spain
http://creativecommons.org/licenses/by-nc-nd/2.5/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 2.5 Spain
http://creativecommons.org/licenses/by-nc-nd/2.5/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 15.301629

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