Reparameterized Scale Mixture of Rayleigh Distribution Regression Models with Varying Precision

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datacite.alternateIdentifier.citationMATHEMATICS,Vol.12,2024
datacite.alternateIdentifier.doi10.3390/math12131982
datacite.creatorRivera, Pilar A.
datacite.creatorGallardo, Diego I.
datacite.creatorVenegas, Osvaldo
datacite.creatorGomez Deniz, Emilio
datacite.creatorGomez, Hector W.
datacite.date2024
datacite.subject.englishscale mixture of Rayleigh distribution
datacite.subject.englishmaximum likelihood estimator
datacite.subject.englishregression models
datacite.subject.englishresiduals
datacite.titleReparameterized Scale Mixture of Rayleigh Distribution Regression Models with Varying Precision
dc.date.accessioned2024-09-10T18:47:10Z
dc.date.available2024-09-10T18:47:10Z
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.identifier.urihttps://repositoriodigital.uct.cl/handle/10925/5955
dc.language.isoen
dc.publisherMDPI
dc.sourceMATHEMATICS
oaire.resourceTypeArticle
uct.indizacionSCI
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