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Title: How to detect linear dependence on the copula level?
Authors: Vladik Kreinovich
Hung T. Nguyen
Songsak Sriboonchitta
Keywords: Computer Science
Issue Date: 1-Jan-2014
Abstract: In many practical situations, the dependence between the quantities is linear or approximately linear. Knowing that the dependence is linear simplifies computations; so, is is desirable to detect linear dependencies. If we know the joint probability distribution, we can detect linear dependence by computing Pearson's correlation coefficient. In practice, we often have a copula instead of a full distribution; in this case, we face a problem of detecting linear dependence based on the copula. Also, distributions are often heavy-tailed, with infinite variances, in which case Pearson's formulas cannot be applied. In this paper, we show how to modify Pearson's formula so that it can be applied to copulas and to heavy-tailed distributions. © Springer International Publishing Switzerland 2014.
ISSN: 21945357
Appears in Collections:CMUL: Journal Articles

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