Extended Half-Power Exponential Distribution with Applications to COVID-19 Data

In this paper, the Extended Half-Power Exponential (EHPE) distribution is built on the basis of the Power Exponential model. The properties of the EHPE model are discussed: the cumulative distribution function, the hazard function, moments, and the skewness and kurtosis coefficients. Estimation is c...

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Detalles Bibliográficos
Autores: Santoro, Karol I., Gómez, Héctor J., Barranco Chamorro, Inmaculada, Gómez, Héctor W.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/134920
Acceso en línea:https://hdl.handle.net/11441/134920
https://doi.org/10.3390/math10060942
Access Level:acceso abierto
Palabra clave:Symmetric distributions
Nonnegative distributions
Kurtosis
Maximum likelihood
COVID-19 data
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spelling Extended Half-Power Exponential Distribution with Applications to COVID-19 DataSantoro, Karol I.Gómez, Héctor J.Barranco Chamorro, InmaculadaGómez, Héctor W.Symmetric distributionsNonnegative distributionsKurtosisMaximum likelihoodCOVID-19 dataIn this paper, the Extended Half-Power Exponential (EHPE) distribution is built on the basis of the Power Exponential model. The properties of the EHPE model are discussed: the cumulative distribution function, the hazard function, moments, and the skewness and kurtosis coefficients. Estimation is carried out by applying maximum likelihood (ML) methods. A Monte Carlo simulation study is carried out to assess the performance of ML estimates. To illustrate the usefulness and applicability of EHPE distribution, two real applications to COVID-19 data in Chile are discussed.MDPIEstadística e Investigación Operativa2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/134920https://doi.org/10.3390/math10060942reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésMathematics, 10, 2-16.https://doi.org/10.3390/math10060942info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1349202026-06-17T12:51:07Z
dc.title.none.fl_str_mv Extended Half-Power Exponential Distribution with Applications to COVID-19 Data
title Extended Half-Power Exponential Distribution with Applications to COVID-19 Data
spellingShingle Extended Half-Power Exponential Distribution with Applications to COVID-19 Data
Santoro, Karol I.
Symmetric distributions
Nonnegative distributions
Kurtosis
Maximum likelihood
COVID-19 data
title_short Extended Half-Power Exponential Distribution with Applications to COVID-19 Data
title_full Extended Half-Power Exponential Distribution with Applications to COVID-19 Data
title_fullStr Extended Half-Power Exponential Distribution with Applications to COVID-19 Data
title_full_unstemmed Extended Half-Power Exponential Distribution with Applications to COVID-19 Data
title_sort Extended Half-Power Exponential Distribution with Applications to COVID-19 Data
dc.creator.none.fl_str_mv Santoro, Karol I.
Gómez, Héctor J.
Barranco Chamorro, Inmaculada
Gómez, Héctor W.
author Santoro, Karol I.
author_facet Santoro, Karol I.
Gómez, Héctor J.
Barranco Chamorro, Inmaculada
Gómez, Héctor W.
author_role author
author2 Gómez, Héctor J.
Barranco Chamorro, Inmaculada
Gómez, Héctor W.
author2_role author
author
author
dc.contributor.none.fl_str_mv Estadística e Investigación Operativa
dc.subject.none.fl_str_mv Symmetric distributions
Nonnegative distributions
Kurtosis
Maximum likelihood
COVID-19 data
topic Symmetric distributions
Nonnegative distributions
Kurtosis
Maximum likelihood
COVID-19 data
description In this paper, the Extended Half-Power Exponential (EHPE) distribution is built on the basis of the Power Exponential model. The properties of the EHPE model are discussed: the cumulative distribution function, the hazard function, moments, and the skewness and kurtosis coefficients. Estimation is carried out by applying maximum likelihood (ML) methods. A Monte Carlo simulation study is carried out to assess the performance of ML estimates. To illustrate the usefulness and applicability of EHPE distribution, two real applications to COVID-19 data in Chile are discussed.
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/134920
https://doi.org/10.3390/math10060942
url https://hdl.handle.net/11441/134920
https://doi.org/10.3390/math10060942
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Mathematics, 10, 2-16.
https://doi.org/10.3390/math10060942
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301629

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