A Bimodal Extension of the Epsilon-Skew-Normal Model

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dc.contributor.authorDuarte, Juan
dc.contributor.authorMartinez Florez, Guillermo
dc.contributor.authorGallardo, Diego Ignacio
dc.contributor.authorVenegas, Osvaldo
dc.contributor.authorGomez, Hector W.
dc.date2023
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.citationMATHEMATICS,Vol.11,,2023
dc.identifier.doi10.3390/math11030507
dc.identifier.urihttps://repositoriodigital.uct.cl/handle/10925/5340
dc.language.isoen
dc.publisherMDPI
dc.sourceMATHEMATICS
dc.subject.englishbimodality
dc.subject.englishepsilon-skew-normal distribution
dc.subject.englishmaximum likelihood estimation
dc.titleA Bimodal Extension of the Epsilon-Skew-Normal Model
dc.typeArticle
uct.indizacionSCI
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