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New Insights on the Multivariate Skew Exponential Power Distribution

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dc.contributor.authorMartín Arevalillo, Jorge
dc.contributor.authorNavarro Veguillas, Hilario
dc.date.accessioned2024-05-20T11:24:40Z
dc.date.available2024-05-20T11:24:40Z
dc.date.issued2023
dc.description.abstractThe multivariate exponential power is a useful distribution for modeling departures from normality in data by means of a tail weight scalar parameter that regulates the non-normality of the model. The incorporation of a shape asymmetry vector into the model serves to account for potential asymmetries and gives rise to the multivariate skew exponential power distribution. This work is aimed at revisiting the skew exponential power distribution taking as a starting point its formulation as a scale mixture of skew-normal distributions. The paper provides some highlights and theoretical insights on the role played by its parameters to assess two complementary aspects of the multivariate non-normality such as directional asymmetry and tail weight behavior regardless of the asymmetry. The intuition behind both issues relies on well-known mathematical ideas about skewness maximization and convex transform stochastic orderings.en
dc.description.versionversión final
dc.identifier.doihttp://doi.org/10.1515/ms-2023-0039
dc.identifier.issn1337-2211
dc.identifier.urihttps://hdl.handle.net/20.500.14468/11922
dc.journal.titleMathematica Slovaca
dc.journal.volume73
dc.language.isoen
dc.publisherDe Gruyter
dc.relation.centerFacultad de Ciencias
dc.relation.departmentEstadística, Investigación Operativa y Cálculo Numérico
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.es
dc.subject.keywordsskew exponential power
dc.subject.keywordsasymmetry
dc.subject.keywordsskewness
dc.subject.keywordskurtosis
dc.subject.keywordstail weight
dc.titleNew Insights on the Multivariate Skew Exponential Power Distributiones
dc.typejournal articleen
dc.typeartículoes
dspace.entity.typePublication
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