A Gamma-type distribution with applications

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datacite.alterneIdentifierISSN-20738994
datacite.date2020
datacite.identifier10.3390/SYM12050870
datacite.sourceSymmetry
dc.contributor.authorIriarte, Yuri A.
dc.contributor.authorVarela, Héctor
dc.contributor.authorGómez, Héctor J.
dc.contributor.authorGómez, Héctor W.
dc.date.accessioned2024-04-22T23:08:51Z
dc.date.available2024-04-22T23:08:51Z
dc.date.issued2020
dc.description.abstractThis article introduces a new probability distribution capable of modeling positive data that present different levels of asymmetry and high levels of kurtosis. A slashed quasi-gamma random variable is defined as the quotient of independent random variables, a generalized gamma is the numerator, and a power of a standard uniform variable is the denominator. The result is a new three-parameter distribution (scale, shape, and kurtosis) that does not present the identifiability problem presented by the generalized gamma distribution. Maximum likelihood (ML) estimation is implemented for parameter estimation. The results of two real data applications revealed a good performance in real settings.
dc.identifier.other10.3390/SYM12050870
dc.identifier.urihttps://repositorioabierto.uantof.cl/handle/uantof/367
dc.language.isoen
dc.publisherMDPI
dc.sourceSymmetry
dc.subjectasymmetry
dc.subjectgeneralized gamma distribution
dc.subjectkurtosis
dc.subjectmaximum likelihood estimation
dc.subjectslash distribution
dc.titleA Gamma-type distribution with applications
dc.typeArticle
oaire.citationEndPage
oaire.citationIssue870
oaire.citationStartPage
oaire.citationVolume12
organization.identifier.rorhttps://ror.org/04eyc6d95
organization.legalNameUniversidad de Antofagasta
uantof.identificator.departmentDepartamento de Matemáticas
uantof.identificator.facultyFacultad de Ciencias Básicas
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