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dc.contributor.authorKlüppelberg, Claudiaen_US
dc.contributor.authorKuhn, Gabrielen_US
dc.description.abstractIn this paper we extend the standard approach of correlation structure analysis in order to reduce the dimension of highdimensional statistical data. The classical assumption of a linear model for the distribution of a random vector is replaced by the weaker assumption of a model for the copula. For elliptical copulae a 'correlation-like' structure remains but different margins and non-existence of moments are possible. Moreover, elliptical copulae allow also for a 'copula structure analysis' of dependence in extremes. After introducing the new concepts and deriving some theoretical results we observe in a simulation study the performance of the estimators: the theoretical asymptotic behavior of the statistics can be observed even for a sample of only 100 observations. Finally, we test our method on real financial data and explain differences between our copula based approach and the classical approach. Our new method yields a considerable dimension reduction also in non-linear models.en_US
dc.publisher|aTechn. Univ.; Sonderforschungsbereich 386, Statistische Analyse Diskreter Strukturen|cMünchenen_US
dc.relation.ispartofseries|aDiscussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München|x507en_US
dc.subject.keywordcopula structure analysisen_US
dc.subject.keywordcorrelation structure analysisen_US
dc.subject.keywordcovariance structure analysisen_US
dc.subject.keyworddimension reductionen_US
dc.subject.keywordelliptical copulaen_US
dc.subject.keywordfactor analysisen_US
dc.subject.keywordKendall's tauen_US
dc.subject.keywordtail copulaen_US
dc.subject.keywordtail dependenceen_US
dc.titleCopula structure analysis based on robust and extreme dependence measuresen_US
dc.type|aWorking Paperen_US

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