Integrating Jackknife into the Theil-Sen Estimator in Multiple Linear Regression Model
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://doi.org/10.57805/revstat.v21i1.398 |
Resumo: | In this study, we provide Theil-Sen parameter estimators, which are in multiple linear regression model based on a spatial median, to be examined by the jackknife method. To obtain the proposed estimator, apply the jackknife to a multivariate Theil-Sen estimator (MTSE) from Dang et al. estimators, who proved that the MTSE estimator is asymptotically normal. Robustness, efficiency, and non-normality of the proposed estimator is tested with simulation studies. As a result, the proposed estimator is shown to be robust, consistent, and more efficient in multiple linear regression models with arbitrary error distributions. Also, it is seen that the proposed estimator reduces the effects of outliers even more and gives more reliable results. So, it is clearly observed that the proposed estimator improves the outcome of the multivariate Theil-Sen estimator. In addition, we support with the aid of numerical examples to these results. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Integrating Jackknife into the Theil-Sen Estimator in Multiple Linear Regression ModeljackkniferobustnessefficiencyTheil-Sen estimatormultiple linear regressionspatial medianIn this study, we provide Theil-Sen parameter estimators, which are in multiple linear regression model based on a spatial median, to be examined by the jackknife method. To obtain the proposed estimator, apply the jackknife to a multivariate Theil-Sen estimator (MTSE) from Dang et al. estimators, who proved that the MTSE estimator is asymptotically normal. Robustness, efficiency, and non-normality of the proposed estimator is tested with simulation studies. As a result, the proposed estimator is shown to be robust, consistent, and more efficient in multiple linear regression models with arbitrary error distributions. Also, it is seen that the proposed estimator reduces the effects of outliers even more and gives more reliable results. So, it is clearly observed that the proposed estimator improves the outcome of the multivariate Theil-Sen estimator. In addition, we support with the aid of numerical examples to these results.Statistics Portugal2023-05-26info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.57805/revstat.v21i1.398https://doi.org/10.57805/revstat.v21i1.398REVSTAT-Statistical Journal; Vol. 21 No. 1 (2023): REVSTAT-Statistical Journal; 97-114REVSTAT; Vol. 21 N.º 1 (2023): REVSTAT-Statistical Journal; 97-1142183-03711645-6726reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAPenghttps://revstat.ine.pt/index.php/REVSTAT/article/view/398https://revstat.ine.pt/index.php/REVSTAT/article/view/398/630Copyright (c) 2021 REVSTAT-Statistical Journalinfo:eu-repo/semantics/openAccessZaman , TolgaAlakuş , Kamil2023-05-27T06:30:14Zoai:revstat:article/398Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:56:25.715933Repositório 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 |
Integrating Jackknife into the Theil-Sen Estimator in Multiple Linear Regression Model |
title |
Integrating Jackknife into the Theil-Sen Estimator in Multiple Linear Regression Model |
spellingShingle |
Integrating Jackknife into the Theil-Sen Estimator in Multiple Linear Regression Model Zaman , Tolga jackknife robustness efficiency Theil-Sen estimator multiple linear regression spatial median |
title_short |
Integrating Jackknife into the Theil-Sen Estimator in Multiple Linear Regression Model |
title_full |
Integrating Jackknife into the Theil-Sen Estimator in Multiple Linear Regression Model |
title_fullStr |
Integrating Jackknife into the Theil-Sen Estimator in Multiple Linear Regression Model |
title_full_unstemmed |
Integrating Jackknife into the Theil-Sen Estimator in Multiple Linear Regression Model |
title_sort |
Integrating Jackknife into the Theil-Sen Estimator in Multiple Linear Regression Model |
author |
Zaman , Tolga |
author_facet |
Zaman , Tolga Alakuş , Kamil |
author_role |
author |
author2 |
Alakuş , Kamil |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Zaman , Tolga Alakuş , Kamil |
dc.subject.por.fl_str_mv |
jackknife robustness efficiency Theil-Sen estimator multiple linear regression spatial median |
topic |
jackknife robustness efficiency Theil-Sen estimator multiple linear regression spatial median |
description |
In this study, we provide Theil-Sen parameter estimators, which are in multiple linear regression model based on a spatial median, to be examined by the jackknife method. To obtain the proposed estimator, apply the jackknife to a multivariate Theil-Sen estimator (MTSE) from Dang et al. estimators, who proved that the MTSE estimator is asymptotically normal. Robustness, efficiency, and non-normality of the proposed estimator is tested with simulation studies. As a result, the proposed estimator is shown to be robust, consistent, and more efficient in multiple linear regression models with arbitrary error distributions. Also, it is seen that the proposed estimator reduces the effects of outliers even more and gives more reliable results. So, it is clearly observed that the proposed estimator improves the outcome of the multivariate Theil-Sen estimator. In addition, we support with the aid of numerical examples to these results. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-05-26 |
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 |
https://doi.org/10.57805/revstat.v21i1.398 https://doi.org/10.57805/revstat.v21i1.398 |
url |
https://doi.org/10.57805/revstat.v21i1.398 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://revstat.ine.pt/index.php/REVSTAT/article/view/398 https://revstat.ine.pt/index.php/REVSTAT/article/view/398/630 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2021 REVSTAT-Statistical Journal info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2021 REVSTAT-Statistical Journal |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Statistics Portugal |
publisher.none.fl_str_mv |
Statistics Portugal |
dc.source.none.fl_str_mv |
REVSTAT-Statistical Journal; Vol. 21 No. 1 (2023): REVSTAT-Statistical Journal; 97-114 REVSTAT; Vol. 21 N.º 1 (2023): REVSTAT-Statistical Journal; 97-114 2183-0371 1645-6726 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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1799131638979887104 |