The skew generalized t distribution as the scale mixture of a skew exponential power distribution and its applications in robust estimation

Basit Öğe Kaydı
dc.authorid0000-0001-7880-5587
dc.authorid0000-0002-7067-4997
dc.contributor.authorArslan, Olcay
dc.contributor.authorGenc, Ali I.
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2009
dc.departmentNiğde ÖHÜ
dc.description.abstractIn this paper, we consider the family of skew generalized t (SGT) distributions originally introduced by Theodossiou [P. Theodossiou, Financial data and the skewed generalized t distribution, Manage. Sci. Part 1 44 (12) ( 1998), pp. 1650-1661] as a skew extension of the generalized t (GT) distribution. The SGT distribution family warrants special attention, because it encompasses distributions having both heavy tails and skewness, and many of the widely used distributions such as Student's t, normal, Hansen's skew t, exponential power, and skew exponential power (SEP) distributions are included as limiting or special cases in the SGT family. We show that the SGT distribution can be obtained as the scale mixture of the SEP and generalized gamma distributions. We investigate several properties of the SGT distribution and consider the maximum likelihood estimation of the location, scale, and skewness parameters under the assumption that the shape parameters are known. We show that if the shape parameters are estimated along with the location, scale, and skewness parameters, the influence function for the maximum likelihood estimators becomes unbounded. We obtain the necessary conditions to ensure the uniqueness of the maximum likelihood estimators for the location, scale, and skewness parameters, with known shape parameters. We provide a simple iterative re-weighting algorithm to compute the maximum likelihood estimates for the location, scale, and skewness parameters and show that this simple algorithm can be identified as an EM-type algorithm. We finally present two applications of the SGT distributions in robust estimation.
dc.identifier.doi10.1080/02331880802401241
dc.identifier.endpage498
dc.identifier.issn0233-1888
dc.identifier.issn1029-4910
dc.identifier.issue5
dc.identifier.scopus2-s2.0-70149104616
dc.identifier.scopusqualityQ3
dc.identifier.startpage481
dc.identifier.urihttps://dx.doi.org/10.1080/02331880802401241
dc.identifier.urihttps://hdl.handle.net/11480/5156
dc.identifier.volume43
dc.identifier.wosWOS:000269603600004
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherTAYLOR & FRANCIS LTD
dc.relation.ispartofSTATISTICS
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectskew exponential power distribution
dc.subjectgeneralized gamma (GG) distribution
dc.subjectgeneralized t (GT) distribution
dc.subjectM-estimation
dc.subjectrobustness
dc.subjectscale mixture distribution
dc.titleThe skew generalized t distribution as the scale mixture of a skew exponential power distribution and its applications in robust estimation
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu