Reparameterized Scale Mixture of Rayleigh DistributionRegression Models with Varying Precision

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dc.contributor.authorRivera, Pilar A.
dc.contributor.authorGallardo, Diego I.
dc.contributor.authorVenegas, Osvaldo
dc.contributor.authorGómez Déniz, Emilio
dc.contributor.authorGómez, Héctor W.
dc.date.accessioned2026-04-06T14:03:49Z
dc.date.available2026-04-06T14:03:49Z
dc.date.issued2024
dc.description.abstractIn this paper, we introduce a new parameterization for the scale mixture of the Rayleigh distribution, which uses a mean linear regression model indexed by mean and precision parameters to model asymmetric positive real data. To test the goodness of fit, we introduce two residuals for the new model. A Monte Carlo simulation study is performed to evaluate the parameter estimation of the proposed model. We compare our proposed model with existing alternatives and illustrate its advantages and usefulness using Gilgais data in R software version 4.2.3 with the gamlss package
dc.description.sponsorshipSemillero UA-2024
dc.identifier.doi10.3390/math12131982
dc.identifier.issn2227-7390
dc.identifier.urihttps://repositorioabierto.uantof.cl/handle/uantof/681
dc.language.isoen
dc.publisherMDPI
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceMathematics
dc.subjectscale mixture of Rayleigh distribution
dc.subjectmaximum likelihood estimator
dc.subjectregression models
dc.subjectresiduals
dc.titleReparameterized Scale Mixture of Rayleigh DistributionRegression Models with Varying Precision
dc.typeArticle
oaire.citation.issue13
oaire.citation.volume12
organization.identifier.rorhttps://ror.org/04eyc6d95
organization.legalNameUniversidad de Antofagasta
uantof.identificator.departmentDepartamento de Estadística y Ciencia de Datos
uantof.identificator.facultyFacultad de Ciencias Básicas
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