Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/72374
Registo completo
Campo DC | Valor | Idioma |
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dc.contributor.author | Shahriari, Shirin | por |
dc.contributor.author | Faria, Susana | por |
dc.contributor.author | Gonçalves, A. Manuela | por |
dc.date.accessioned | 2021-04-29T18:45:49Z | - |
dc.date.available | 2021-04-29T18:45:49Z | - |
dc.date.issued | 2019-03-28 | - |
dc.date.submitted | 2017-05-09 | - |
dc.identifier.issn | 0361-0918 | por |
dc.identifier.uri | https://hdl.handle.net/1822/72374 | - |
dc.description.abstract | 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. | por |
dc.description.sponsorship | 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. | por |
dc.language.iso | eng | por |
dc.publisher | Taylor & Francis | por |
dc.relation | info:eu-repo/grantAgreement/FCT/SFRH/SFRH%2FBD%2F51164%2F2010/PT | por |
dc.relation | PEst-OE/MAT/UI0013/2017 | por |
dc.rights | openAccess | por |
dc.subject | Jackknife | por |
dc.subject | Outlier detection | por |
dc.subject | Robust variable selection | por |
dc.subject | Sparsity | por |
dc.title | A robust sparce linear approach for contamined data | por |
dc.type | article | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://doi.org/10.1080/03610918.2019.1588304 | por |
oaire.citationStartPage | 1 | por |
oaire.citationEndPage | 17 | por |
oaire.citationIssue | 6 | por |
oaire.citationVolume | 50 | por |
dc.identifier.eissn | 1532-4141 | por |
dc.identifier.doi | 10.1080/03610918.2019.1588304 | por |
dc.subject.fos | Ciências Naturais::Matemáticas | por |
dc.subject.wos | Science & Technology | por |
sdum.journal | Communications in Statistics - Simulation and Computation | por |
oaire.version | VoR | por |
Aparece nas coleções: | CMAT - Artigos em revistas com arbitragem / Papers in peer review journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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A robust sparse linear approach for contaminated data.pdf | Article | 1,87 MB | Adobe PDF | Ver/Abrir |