A copula function can be employed to decompose the information content of a multivariate distribution into marginal and dependence components, with the latter quantified by the mutual infromation. From this statement, it is possible to state that a link between infromation and copula theories is valid. On the basis of these results, in the paper we show as it is possibleto use the independent component analysis to estimate the mutual information of a multivariate random and, then, to select the model of copula which better interprets the dependence in sample data.
Palmitesta, P., Provasi, C. (2012). Copula Component Analysis for Dependence Modelling. QUADERNI DI STATISTICA, 14, 185-188.
Copula Component Analysis for Dependence Modelling
PALMITESTA, PAOLA;
2012-01-01
Abstract
A copula function can be employed to decompose the information content of a multivariate distribution into marginal and dependence components, with the latter quantified by the mutual infromation. From this statement, it is possible to state that a link between infromation and copula theories is valid. On the basis of these results, in the paper we show as it is possibleto use the independent component analysis to estimate the mutual information of a multivariate random and, then, to select the model of copula which better interprets the dependence in sample data.
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https://hdl.handle.net/11365/26515
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