Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorFischer, Matthias J.en_US
dc.description.abstractOne possibility to construct heavy tail distributions is to directly manipulate a standard Gaussian random variable by means of transformations which satisfy certain conditions. This approach dates back to Tukey (1960) who introduces the popular H-transformation. Alternatively, the K-transformation of MacGillivray & Cannon (1997) or the J-transformation of Fischer & Klein (2004) may be used. Recently, Klein & Fischer (2006) proposed a very general power kurtosis transformation which includes the above-mentioned transformations as special cases. Unfortunately, their transformation requires an infinite number of unknown parameters to be estimated. In contrast, we introduce a very simple method to construct êexible kurtosis transformations. In particular, manageable superstructures are suggested in order to statistically discriminate between H-, J-and K-distributions (associated to H-, J- and K-transformations).en_US
dc.publisher|aUniversität Erlangen-Nürnberg, Lehrstuhl für Statistik und empirische Wirtschaftsforschung |cNürnbergen_US
dc.relation.ispartofseries|aDiskussionspapiere // Friedrich-Alexander-Universität Erlangen-Nürnberg, Lehrstuhl für Statistik und Ökonometrie |x73/2006en_US
dc.subject.keywordGeneralized kurtosis transformationen_US
dc.titleA note on the construction of generalized Tukey-type transformationsen_US
dc.type|aWorking Paperen_US

Files in This Item:
217.92 kB

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.