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dc.contributor.authorVladik Kreinovichen_US
dc.contributor.authorHung T. Nguyenen_US
dc.contributor.authorSongsak Sriboonchittaen_US
dc.description.abstractIn 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.en_US
dc.subjectComputer Scienceen_US
dc.titleHow to detect linear dependence on the copula level?en_US
dc.typeBook Seriesen_US
article.title.sourcetitleAdvances in Intelligent Systems and Computingen_US
article.volume251en_US of Texas at El Pasoen_US Mexico State University Las Crucesen_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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