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- W1716056536 abstract "In 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." @default.
- W1716056536 created "2016-06-24" @default.
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- W1716056536 date "2006-01-01" @default.
- W1716056536 modified "2023-09-23" @default.
- W1716056536 title "Copula Structure Analysis Based on Robust and Extreme Dependence Measures" @default.
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- W1716056536 doi "https://doi.org/10.5282/ubm/epub.1871" @default.
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