Abstract
Dependence structure, e.g. measures of dependence, is one of the main studies in correlation analysis. In [10], B. Schweizer and E.F. Wolff used L\(^{p}\)-metric \(d_{L^{p}}(C,P)\) to obtain a measure of monotone dependence where P is the product copula or independent copula, and in [11] P. A. Stoimenov defined Sobolev metric \(d_{S}(C,P)\) to construct the measure \(\omega (C)\) for a class of Mutual Complete Dependences (MCDs). Due to the fact that the class of monotone dependence is contained in the class of MCDs, we constructed a new measure of monotone dependence, \(\lambda (C),\) based on Sobolev metric which can be used to characterize comonotonic, countermonotonic and independence.
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Tran, H.D., Pham, U.H., Ly, S., Vo-Duy, T. (2015). A New Measure of Monotone Dependence by Using Sobolev Norms for Copula. In: Huynh, VN., Inuiguchi, M., Demoeux, T. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2015. Lecture Notes in Computer Science(), vol 9376. Springer, Cham. https://doi.org/10.1007/978-3-319-25135-6_13
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DOI: https://doi.org/10.1007/978-3-319-25135-6_13
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