Displaying similar documents to “Bounds for trivariate copulas with given bivariate marginals.”

Componentwise concave copulas and their asymmetry

Fabrizio Durante, Pier Luigi Papini (2009)

Kybernetika

Similarity:

The class of componentwise concave copulas is considered, with particular emphasis on its closure under some constructions of copulas (e.g., ordinal sum) and its relations with other classes of copulas characterized by some notions of concavity and/or convexity. Then, a sharp upper bound is given for the L -measure of non-exchangeability for copulas belonging to this class.

Constructing copulas by means of pairs of order statistics

Ali Dolati, Manuel Úbeda-Flores (2009)

Kybernetika

Similarity:

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.

Univariate conditioning of copulas

Radko Mesiar, Vladimír Jágr, Monika Juráňová, Magda Komorníková (2008)

Kybernetika

Similarity:

The univariate conditioning of copulas is studied, yielding a construction method for copulas based on an a priori given copula. Based on the gluing method, g-ordinal sum of copulas is introduced and a representation of copulas by means of g-ordinal sums is given. Though different right conditionings commute, this is not the case of right and left conditioning, with a special exception of Archimedean copulas. Several interesting examples are given. Especially, any Ali-Mikhail-Haq copula...

Extreme distribution functions of copulas

Manuel Úbeda-Flores (2008)

Kybernetika

Similarity:

In this paper we study some properties of the distribution function of the random variable C(X,Y) when the copula of the random pair (X,Y) is M (respectively, W) – the copula for which each of X and Y is almost surely an increasing (respectively, decreasing) function of the other –, and C is any copula. We also study the distribution functions of M(X,Y) and W(X,Y) given that the joint distribution function of the random variables X and Y is any copula.