Robust modeling using the generalized epsilon-skew-t distribution

Simple item page
datacite.alternateIdentifier.citationJOURNAL OF APPLIED STATISTICS,Vol.39,2685-2698,2012
datacite.alternateIdentifier.doi10.1080/02664763.2012.725462
datacite.creatorVenegas, Osvaldo
datacite.creatorRodriguez, Francisco
datacite.creatorGomez, Hector W.
datacite.creatorOlivares Pacheco, Juan F.
datacite.creatorBolfarine, Heleno
datacite.date2012
datacite.subject.englishgeneralized Student-t distribution
datacite.subject.englishstochastic representation
datacite.subject.englishasymmetry coefficient
datacite.subject.englishkurtosis coefficient
datacite.titleRobust modeling using the generalized epsilon-skew-t distribution
dc.date.accessioned2021-04-30T17:05:04Z
dc.date.available2021-04-30T17:05:04Z
dc.description.abstractIn this paper, an alternative skew Student-t family of distributions is studied. It is obtained as an extension of the generalized Student-t (GS-t) family introduced by McDonald and Newey [10]. The extension that is obtained can be seen as a reparametrization of the skewed GS-t distribution considered by Theodossiou [14]. A key element in the construction of such an extension is that it can be stochastically represented as a mixture of an epsilon-skew-power-exponential distribution [1] and a generalized-gamma distribution. From this representation, we can readily derive theoretical properties and easy-to-implement simulation schemes. Furthermore, we study some of its main properties including stochastic representation, moments and asymmetry and kurtosis coefficients. We also derive the Fisher information matrix, which is shown to be nonsingular for some special cases such as when the asymmetry parameter is null, that is, at the vicinity of symmetry, and discuss maximum-likelihood estimation. Simulation studies for some particular cases and real data analysis are also reported, illustrating the usefulness of the extension considered.
dc.identifier.urihttp://repositoriodigital.uct.cl/handle/10925/3923
dc.language.isoen
dc.publisherTAYLOR & FRANCIS LTD
dc.sourceJOURNAL OF APPLIED STATISTICS
oaire.resourceTypeArticle
uct.catalogadorWOS
uct.indizacionSCI
Files
Collections
MigraciĆ³n Web of Science