On q-Generalized Extreme Values under Power Normalization with Properties, Estimation Methods and Applications to Covid-19 Data
Autor(a) principal: | |
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Data de Publicação: | 2024 |
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.v22i1.456 |
Resumo: | This paper introduces the q-analogues of the generalized extreme value distribution and its discrete counterpart under power normalization. The inclusion of the parameter q enhances modeling flexibility. The continuous extended model can produce various types of hazard rate functions, with supports that can be finite, infinite, or bounded above or below. Additionally, these new models can effectively handle skewed data, particularly those with highly extreme observations. Statistical properties of the proposed continuous distribution are presented, and the model parameters are estimated using various approaches. A simulation study evaluates the performance of the estimators across different sample sizes. Finally, three distinct real datasets are analyzed to demonstrate the versatility of the proposed model. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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On q-Generalized Extreme Values under Power Normalization with Properties, Estimation Methods and Applications to Covid-19 Dataextreme value theorysurvival discretization approachentropyestimation methodssimulationCOVID-19This paper introduces the q-analogues of the generalized extreme value distribution and its discrete counterpart under power normalization. The inclusion of the parameter q enhances modeling flexibility. The continuous extended model can produce various types of hazard rate functions, with supports that can be finite, infinite, or bounded above or below. Additionally, these new models can effectively handle skewed data, particularly those with highly extreme observations. Statistical properties of the proposed continuous distribution are presented, and the model parameters are estimated using various approaches. A simulation study evaluates the performance of the estimators across different sample sizes. Finally, three distinct real datasets are analyzed to demonstrate the versatility of the proposed model.Statistics Portugal2024-02-22info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.57805/revstat.v22i1.456https://doi.org/10.57805/revstat.v22i1.456REVSTAT-Statistical Journal; Vol. 22 No. 1 (2024): REVSTAT-Statistical Journal; 61–86REVSTAT; Vol. 22 N.º 1 (2024): REVSTAT-Statistical Journal; 61–862183-03711645-6726reponame: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/456https://revstat.ine.pt/index.php/REVSTAT/article/view/456/684Copyright (c) 2024 REVSTAT-Statistical Journalinfo:eu-repo/semantics/openAccessEliwa, Mohamed S.Abo Zaid , E. O.El-Morshedy , Mahmoud2024-02-24T07:12:42Zoai:revstat:article/456Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:11:18.483124Repositório 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 |
On q-Generalized Extreme Values under Power Normalization with Properties, Estimation Methods and Applications to Covid-19 Data |
title |
On q-Generalized Extreme Values under Power Normalization with Properties, Estimation Methods and Applications to Covid-19 Data |
spellingShingle |
On q-Generalized Extreme Values under Power Normalization with Properties, Estimation Methods and Applications to Covid-19 Data Eliwa, Mohamed S. extreme value theory survival discretization approach entropy estimation methods simulation COVID-19 |
title_short |
On q-Generalized Extreme Values under Power Normalization with Properties, Estimation Methods and Applications to Covid-19 Data |
title_full |
On q-Generalized Extreme Values under Power Normalization with Properties, Estimation Methods and Applications to Covid-19 Data |
title_fullStr |
On q-Generalized Extreme Values under Power Normalization with Properties, Estimation Methods and Applications to Covid-19 Data |
title_full_unstemmed |
On q-Generalized Extreme Values under Power Normalization with Properties, Estimation Methods and Applications to Covid-19 Data |
title_sort |
On q-Generalized Extreme Values under Power Normalization with Properties, Estimation Methods and Applications to Covid-19 Data |
author |
Eliwa, Mohamed S. |
author_facet |
Eliwa, Mohamed S. Abo Zaid , E. O. El-Morshedy , Mahmoud |
author_role |
author |
author2 |
Abo Zaid , E. O. El-Morshedy , Mahmoud |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Eliwa, Mohamed S. Abo Zaid , E. O. El-Morshedy , Mahmoud |
dc.subject.por.fl_str_mv |
extreme value theory survival discretization approach entropy estimation methods simulation COVID-19 |
topic |
extreme value theory survival discretization approach entropy estimation methods simulation COVID-19 |
description |
This paper introduces the q-analogues of the generalized extreme value distribution and its discrete counterpart under power normalization. The inclusion of the parameter q enhances modeling flexibility. The continuous extended model can produce various types of hazard rate functions, with supports that can be finite, infinite, or bounded above or below. Additionally, these new models can effectively handle skewed data, particularly those with highly extreme observations. Statistical properties of the proposed continuous distribution are presented, and the model parameters are estimated using various approaches. A simulation study evaluates the performance of the estimators across different sample sizes. Finally, three distinct real datasets are analyzed to demonstrate the versatility of the proposed model. |
publishDate |
2024 |
dc.date.none.fl_str_mv |
2024-02-22 |
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.v22i1.456 https://doi.org/10.57805/revstat.v22i1.456 |
url |
https://doi.org/10.57805/revstat.v22i1.456 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://revstat.ine.pt/index.php/REVSTAT/article/view/456 https://revstat.ine.pt/index.php/REVSTAT/article/view/456/684 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2024 REVSTAT-Statistical Journal info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2024 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. 22 No. 1 (2024): REVSTAT-Statistical Journal; 61–86 REVSTAT; Vol. 22 N.º 1 (2024): REVSTAT-Statistical Journal; 61–86 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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1799137763350544384 |