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dc.contributor.authorHung T. Nguyenen_US
dc.contributor.authorVladik Kreinovichzen_US
dc.contributor.authorBerlin Wuen_US
dc.description.abstract© 2014 by the Mathematical Association of Thailand. All rights reserved. Copulas are a general way of describing dependence between two or more random variables. When we only have partial information about the dependence, i.e., when several different copulas are consistent with our knowledge, it is often necessary to select one of these copulas. A frequently used method of selecting this copula is the maximum entropy approach, when we select a copula with the largest entropy. However, in some cases, the maximum entropy approach leads to an unreasonable selection – e.g., even if we know that the two random variables are positively correlated, the maximum entropy approach completely ignores this information. In this paper, we show how to properly modify the maximum entropy approach so that it will lead to more reasonable results: by applying this approach not to the probabilities themselves, but to “second order” probabilities – i.e., probabilities of different probability distributions.en_US
dc.titleUsing second-order probabilities to make maximum entropy approach to copulas more reasonableen_US
article.title.sourcetitleThai Journal of Mathematicsen_US
article.volume2014en_US Mexico State University Las Crucesen_US Mai Universityen_US of Texas at El Pasoen_US Chengchi Universityen_US
Appears in Collections:CMUL: Journal Articles

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