‎One of the most useful tools for handling multivariate distributions of dependent variables in terms of their marginal distribution is a copula function‎. ‎The copula families capture a fair amount of attention due to their applicability and flexibility in describing the non-Gaussian spatial dependent data‎. ‎The particular properties of the spatial copula are rarely ...

In this paper, we consider different issues related to Archimedean copulae and positive dependence. In the first part, we characterize Archimedean copulae that possess positive dependence properties such as multivariate total positivity of order 2 ðMTP2Þ and conditionally increasingness in sequence. In the second part, we investigate conditions for exchangeable binary sequences to admit an Arch...

Convergence of a sequence of bivariate Archimedean copulas to another Archimedean copula or to the comonotone copula is shown to be equivalent with convergence of the corresponding sequence of Kendall distribution functions. No extra differentiability conditions on the generators are needed. r 2007 Elsevier B.V. All rights reserved.

The copula function is a multivariate distribution whose marginal distributions are uniformly distributed on the interval [0,1], this function called copula that ties the joint and the margins together. One important class of copula models is that of semiparametric copula models. In this paper, a semiparametric copula and its properties are introduced also a test of symmetry for semiparametric ...

A maximum entropy copula is the copula associated with the joint distribution, with prescribed marginal distributions on [0, 1], which maximizes the Tsallis–Havrda–Chavát entropy with q = 2. We find necessary and sufficient conditions for each maximum entropy copula to be a copula in the class introduced in Rodríguez-Lallena and Úbeda-Flores (2004), and we also show that each copula in that cla...

We propose a new framework for dependence structure learning via copula. Copula is a statistical theory on dependence and measurement of association. Graphical models are considered as a type of special case of copula families, named product copula. In this paper, a nonparametric algorithm for copula estimation is presented. Then a Chow-Liu like method based on dependence measure via copula is ...

The multivariate distribution of five main indices of Tehran stock exchange is approximated using a pair-copula model. A vine graphical model is used to produce an í µí±›-dimensional copula. This is accomplished using a flexible copula called a minimum information (MI) copula as a part of pair-copula construction. Obtained results show that the achieved model has a good level of approximation.

In this paper we discuss the problem on parametric and non parametric estimation of the distributions generated by the Marshall-Olkin copula. This copula comes from the Marshall-Olkin bivariate exponential distribution used in reliability analysis. Through this copula we can extend the Marshall-Olkin distribution in order to construct several bivariate survival functions. The cumulative distrib...

The measure for expert dependence proposed by Jouini and Clemen (clemen) is implemented for expert judgement data gathered at the T.U. Delft. Experts show less dependence than might have been supposed, though more sensitive measures might reveal more. Clemen’s copula for aggregation is implemented and performance is compared with performance-based combinations for two illustrative cases.