Publicação
A robust sparse linear approach for contaminated data
| 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. |
|---|---|
| Autores principais: | Shahriari, S |
| Outros Autores: | Faria, S; Manuela Gonçalves, A |
| Assunto: | JackKnife Outlier detection Robust variable selection Sparsity |
| Ano: | 2019 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Universidade do Porto |
| Idioma: | inglês |
| Origem: | Repositório Aberto da Universidade do Porto |
| _version_ | 1867768468109000704 |
|---|---|
| author | Shahriari, S |
| author2 | Faria, S Manuela Gonçalves, A |
| author2_role | author author |
| author_facet | Shahriari, S Faria, S Manuela Gonçalves, A |
| author_role | author |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Shahriari, S\"},{\"Person.name\":\"Faria, S\"},{\"Person.name\":\"Manuela Gonçalves, A\"}] |
| datacite.creators.creator.creatorName.fl_str_mv | Shahriari, S Faria, S Manuela Gonçalves, A |
| datacite.date.Accepted.fl_str_mv | 2019-01-01T00:00:00Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_16ec |
| datacite.subjects.subject.fl_str_mv | JackKnife Outlier detection Robust variable selection Sparsity |
| datacite.titles.title.fl_str_mv | A robust sparse linear approach for contaminated data |
| dc.creator.none.fl_str_mv | Shahriari, S Faria, S Manuela Gonçalves, A |
| dc.date.Accepted.fl_str_mv | 2019-01-01T00:00:00Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://hdl.handle.net/10216/149717 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Taylor & Francis |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_16ec |
| dc.subject.none.fl_str_mv | JackKnife Outlier detection Robust variable selection Sparsity |
| dc.title.fl_str_mv | A robust sparse linear approach for contaminated data |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| 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. |
| dirty | 0 |
| eu_rights_str_mv | restrictedAccess |
| format | article |
| id | rabertoup_4cc820bf79a7568ee670bc42e7e5fdfd |
| identifier.url.fl_str_mv | https://hdl.handle.net/10216/149717 |
| instacron_str | UP |
| institution | Universidade do Porto |
| instname_str | Universidade do Porto |
| language | eng |
| network_acronym_str | rabertoup |
| network_name_str | Repositório Aberto da Universidade do Porto |
| oai_identifier_str | oai:repositorio-aberto.up.pt:10216/149717 |
| organization_str_mv | urn:organizationAcronym:up |
| person_str_mv | Shahriari, S Faria, S Manuela Gonçalves, A |
| publishDate | 2019 |
| publisher.none.fl_str_mv | Taylor & Francis |
| reponame_str | Repositório Aberto da Universidade do Porto |
| repository_id_str | urn:repositoryAcronym:rabertoup |
| service_str_mv | urn:repositoryAcronym:rabertoup |
| spelling | 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.application/pdfengTaylor & FrancisA robust sparse linear approach for contaminated dataShahriari, SFaria, SManuela Gonçalves, AHandlehttps://hdl.handle.net/10216/149717ISSNIsPartOf0361-0918ISSNIsPartOf1532-4141DOIIsPartOf10.1080/03610918.2019.158830420192019-01-01T00:00:00Zhttp://purl.org/coar/access_right/c_16ecrestricted accessJackKnifeOutlier detectionRobust variable selectionSparsity1818494 byteshttp://purl.org/coar/access_right/c_16ecapplication/pdffulltexthttps://repositorio-aberto.up.pt/bitstream/10216/149717/1/shahriari-cssc-2021.pdfliteraturehttp://purl.org/coar/resource_type/c_6501journal article |
| spellingShingle | A robust sparse linear approach for contaminated data Shahriari, S JackKnife Outlier detection Robust variable selection Sparsity |
| status | SINGLETON |
| subject.fl_str_mv | JackKnife Outlier detection Robust variable selection Sparsity |
| title | A robust sparse linear approach for contaminated data |
| title_full | A robust sparse linear approach for contaminated data |
| title_fullStr | A robust sparse linear approach for contaminated data |
| title_full_unstemmed | A robust sparse linear approach for contaminated data |
| title_short | A robust sparse linear approach for contaminated data |
| title_sort | A robust sparse linear approach for contaminated data |
| topic | JackKnife Outlier detection Robust variable selection Sparsity |
| topic_facet | JackKnife Outlier detection Robust variable selection Sparsity |
| url | https://hdl.handle.net/10216/149717 |
| visible | 1 |