A Bimodal Extension of the Epsilon-Skew-Normal Model

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datacite.alternateIdentifier.citationMATHEMATICS,Vol.11,,2023
datacite.alternateIdentifier.doi10.3390/math11030507
datacite.creatorDuarte, Juan
datacite.creatorMartinez Florez, Guillermo
datacite.creatorGallardo, Diego Ignacio
datacite.creatorVenegas, Osvaldo
datacite.creatorGomez, Hector W.
datacite.date2023
datacite.subject.englishbimodality
datacite.subject.englishepsilon-skew-normal distribution
datacite.subject.englishmaximum likelihood estimation
datacite.titleA Bimodal Extension of the Epsilon-Skew-Normal Model
dc.date.accessioned2023-06-08T15:48:18Z
dc.date.available2023-06-08T15:48:18Z
dc.description.abstractThis article introduces a bimodal model based on the epsilon-skew-normal distribution. This extension generates bimodal distributions similar to those produced by the mixture of normal distributions. We study the basic properties of this new family. We apply maximum likelihood estimators, calculate the information matrix and present a simulation study to assess parameter recovery. Finally, we illustrate the results to three real data sets, suggesting this new distribution as a plausible alternative for modelling bimodal data.
dc.identifier.urihttps://repositoriodigital.uct.cl/handle/10925/5340
dc.language.isoen
dc.publisherMDPI
dc.sourceMATHEMATICS
oaire.resourceTypeArticle
uct.indizacionSCI
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