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- W2030696482 abstract "Summary We extend the standard approach of correlation structure analysis for dimension reduction of high dimensional 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 copulas a correlation-like structure remains, but different margins and non-existence of moments are possible. After introducing the new concept and deriving some theoretical results we observe in a simulation study the performance of the estimators: the theoretical asymptotic behaviour of the statistics can be observed even for small sample sizes. Finally, we show our method at work for a financial data set and explain differences between our copula-based approach and the classical approach. Our new method yielear models also." @default.
- W2030696482 created "2016-06-24" @default.
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- W2030696482 date "2009-06-01" @default.
- W2030696482 modified "2023-10-18" @default.
- W2030696482 title "Copula Structure Analysis" @default.
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- W2030696482 doi "https://doi.org/10.1111/j.1467-9868.2009.00707.x" @default.
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