Parametric Quantile Regression Models for Fitting Double Bounded Response with Application to COVID-19 Mortality Rate Data

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datacite.alternateIdentifier.citationMathematics, 10 (13), 2022
datacite.alternateIdentifier.doi10.3390/math10132249
datacite.alternateIdentifier.issn2227-7390
datacite.creatorGallardo, Diego Ignacio
datacite.creatorBourguignon, Marcelo
datacite.creatorGómez, Yolanda M.
datacite.creatorCaamaño-Carrillo, Christian
datacite.creatorVenegas, Osvaldo
datacite.date2022
datacite.rightsAcceso abierto
datacite.subjectCovid-19
datacite.subjectParametric Quantile Regression
datacite.subjectPower Johnson Sb Distribution
datacite.subjectProportion
datacite.titleParametric Quantile Regression Models for Fitting Double Bounded Response with Application to COVID-19 Mortality Rate Data
dc.contributor.authorVENEGAS TORRES, OSVALDO
dc.description.abstractIn this paper, we develop two fully parametric quantile regression models, based on the power Johnson S<inf>B</inf> distribution for modeling unit interval response in different quantiles. In particular, the conditional distribution is modeled by the power Johnson S<inf>B</inf> distribution. The maximum likelihood (ML) estimation method is employed to estimate the model parameters. Simulation studies are conducted to evaluate the performance of the ML estimators in finite samples. Furthermore, we discuss influence diagnostic tools and residuals. The effectiveness of our proposals is illustrated with a data set of the mortality rate of COVID-19 in different countries. The results of our models with this data set show the potential of using the new methodology. Thus, we conclude that the results are favorable to the use of proposed quantile regression models for fitting double bounded data. © 2022 Elsevier B.V., All rights reserved.
dc.description.ia_keywordmodels, data, quantile, regression, distribution, parametric, power
dc.formatPDF
dc.identifier.urihttps://repositoriodigital.uct.cl/handle/10925/4618
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relationinstname: ANID
dc.relationreponame: Repositorio Digital RI2.0
dc.rights.driverinfo:eu-repo/semantics/openAccess
dc.sourceMathematics
dc.subject.ia_odsODS 3: Salud y bienestar
dc.subject.ia_oecd1nCiencias Naturales
dc.subject.ia_oecd2nMatemáticas y Estadística
dc.subject.ia_oecd3nEstadística
dc.type.driverinfo:eu-repo/semantics/article
dc.type.driverhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.type.openaireinfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublication
oaire.citationEdition2022
oaire.citationIssue13
oaire.citationTitleMathematics
oaire.citationVolume10
oaire.fundingReferenceANID FONDECYT 11220066 (Regular)
oaire.fundingReferenceUBB DIUBB 2120538 IF/R
oaire.licenseConditionObra bajo licencia Creative Commons Atribución 4.0 Internacional
oaire.licenseCondition.urihttps://creativecommons.org/licenses/by/4.0/
oaire.resourceTypeArtículo
oaire.resourceType.enArticle
relation.isAuthorOfPublicationf22c7aed-a907-4211-b78a-6fef24d7e4df
relation.isAuthorOfPublication.latestForDiscoveryf22c7aed-a907-4211-b78a-6fef24d7e4df
uct.catalogadorjvu
uct.comunidadIngenieríaen_US
uct.departamentoDepartamento de Ciencias Matemáticas y Físicas
uct.facultadFacultad de Ingeniería
uct.indizacionScience Citation Index Expanded - SCIE
uct.indizacionScopus
uct.indizacionzbMATH
uct.indizacionMathSciNet
uct.indizacionDOAJ
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