A robust sparce linear approach for contamined data

Detalhes bibliográficos
Autor(a) principal: Shahriari, Shirin
Data de Publicação: 2019
Outros Autores: Faria, Susana, Gonçalves, A. Manuela
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/1822/72374
Resumo: A challenging problem in a linear regression model is to select a parsimonious model which is robust to the presence of contamination in the data. In this paper, we present a sparse linear approach which detects outliers by using a highly robust regression method. The model with optimal predictive ability as measured by the median absolute deviation of the prediction errors on JackKnife subsets is used to detect outliers. The performance of the proposed method is evaluated on a simulation study with a different type of outliers and high leverage points and also on a real data set.
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spelling A robust sparce linear approach for contamined dataJackknifeOutlier detectionRobust variable selectionSparsityCiências Naturais::MatemáticasScience & TechnologyA challenging problem in a linear regression model is to select a parsimonious model which is robust to the presence of contamination in the data. In this paper, we present a sparse linear approach which detects outliers by using a highly robust regression method. The model with optimal predictive ability as measured by the median absolute deviation of the prediction errors on JackKnife subsets is used to detect outliers. The performance of the proposed method is evaluated on a simulation study with a different type of outliers and high leverage points and also on a real data set.The authors would like to thank to the Associate Editor and the reviewers for their useful com ments which led to a considerable improvement of the manuscript. This work was supported by FEDER Funds through “Programa Operacional Factores de Competitividade-COMPETE” and by Portuguese Funds through FCT “Fundação para a Ciência e a Tecnologia”, within the SFRH/BD/51164/2010 and PEst-OE/MAT/UI0013/2017.Taylor & FrancisUniversidade do MinhoShahriari, ShirinFaria, SusanaGonçalves, A. Manuela2019-03-282019-03-28T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/72374eng0361-09181532-414110.1080/03610918.2019.1588304https://doi.org/10.1080/03610918.2019.1588304info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:52:13Zoai:repositorium.sdum.uminho.pt:1822/72374Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:51:17.354343Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A robust sparce linear approach for contamined data
title A robust sparce linear approach for contamined data
spellingShingle A robust sparce linear approach for contamined data
Shahriari, Shirin
Jackknife
Outlier detection
Robust variable selection
Sparsity
Ciências Naturais::Matemáticas
Science & Technology
title_short A robust sparce linear approach for contamined data
title_full A robust sparce linear approach for contamined data
title_fullStr A robust sparce linear approach for contamined data
title_full_unstemmed A robust sparce linear approach for contamined data
title_sort A robust sparce linear approach for contamined data
author Shahriari, Shirin
author_facet Shahriari, Shirin
Faria, Susana
Gonçalves, A. Manuela
author_role author
author2 Faria, Susana
Gonçalves, A. Manuela
author2_role author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Shahriari, Shirin
Faria, Susana
Gonçalves, A. Manuela
dc.subject.por.fl_str_mv Jackknife
Outlier detection
Robust variable selection
Sparsity
Ciências Naturais::Matemáticas
Science & Technology
topic Jackknife
Outlier detection
Robust variable selection
Sparsity
Ciências Naturais::Matemáticas
Science & Technology
description A challenging problem in a linear regression model is to select a parsimonious model which is robust to the presence of contamination in the data. In this paper, we present a sparse linear approach which detects outliers by using a highly robust regression method. The model with optimal predictive ability as measured by the median absolute deviation of the prediction errors on JackKnife subsets is used to detect outliers. The performance of the proposed method is evaluated on a simulation study with a different type of outliers and high leverage points and also on a real data set.
publishDate 2019
dc.date.none.fl_str_mv 2019-03-28
2019-03-28T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1822/72374
url http://hdl.handle.net/1822/72374
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0361-0918
1532-4141
10.1080/03610918.2019.1588304
https://doi.org/10.1080/03610918.2019.1588304
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Taylor & Francis
publisher.none.fl_str_mv Taylor & Francis
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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