Pier Luigi Papini (2015)
Dependence Modeling
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The present paper is related to the study of asymmetry for copulas by introducing functionals based on different norms for continuous variables. In particular, we discuss some facts concerning asymmetry and we point out some flaws occurring in the recent literature dealing with this matter.
Fabrizio Durante, Giovanni Puccetti, Matthias Scherer, Steven Vanduffel (2017)
Dependence Modeling
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Ali Dolati, Manuel Úbeda-Flores (2009)
Kybernetika
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In this paper, we introduce two transformations on a given copula to construct new and recover already-existent families. The method is based on the choice of pairs of order statistics of the marginal distributions. Properties of such transformations and their effects on the dependence and symmetry structure of a copula are studied.
Tarad Jwaid, Bernard de Baets, Hans de Meyer (2009)
Kybernetika
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We introduce four families of semilinear copulas (i.e. copulas that are linear in at least one coordinate of any point of the unit square) of which the diagonal and opposite diagonal sections are given functions. For each of these families, we provide necessary and sufficient conditions under which given diagonal and opposite diagonal functions can be the diagonal and opposite diagonal sections of a semilinear copula belonging to that family. We focus particular attention on the family...
Fabrizio Durante, Juan Fernández-Sánchez (2011)
Kybernetika
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We describe a class of bivariate copulas having a fixed diagonal section. The obtained class contains both the Fréchet upper and lower bounds and it allows to describe non-trivial tail dependence coefficients along both the diagonals of the unit square.
Fabrizio Durante, Juan Fernández-Sánchez, Wolfgang Trutschnig (2014)
Dependence Modeling
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We solve a recent open problem about a new transformation mapping the set of copulas into itself. The obtained mapping is characterized in algebraic terms and some limit results are proved.
Sebastian Fuchs (2016)
Dependence Modeling
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We study measures of concordance for multivariate copulas and copulas that induce measures of concordance. To this end, for a copula A, we consider the maps C → R given by [...] where C denotes the collection of all d–dimensional copulas, M is the Fréchet–Hoeffding upper bound, Π is the product copula, [. , .] : C × C → R is the biconvex form given by [C, D] := ∫ [0,1]d C(u) dQD(u) with the probability measure QD associated with the copula D, and ψΛ C → C is a transformation of copulas....
Erich Peter Klement, Radko Mesiar, Endre Pap (2002)
Kybernetika
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