In recent joint papers with B. Schweizer, we used the notion of a copula to introduce a family of symmetric, nonparametric measures of dependence of two random variables. Here, we present n-dimensional extensions of these measures and of Spearman's ro. We study them vis-a-vis appropriate higher dimensional analogues of Rényi's axioms for measures of dependence, determine relations among them, and in some cases establish reduction formulae for their computation
The paper presents a new copula based method for measuring dependence between random variables. Our ...
Restricted until 15 Feb. 2009.A construction of multivariate distribution functions that allows for ...
In this paper we extend the standard approach of correlation structure analysis in order to reduce t...
The paper is devoted to the multivariate measures of dependence. In contrast to the classical approa...
AbstractA new family of conditional-dependence measures based on Spearman's rho is introduced. The c...
We proposed a new statistical dependency measure called Copula Dependency Coefficient(CDC) for two s...
AbstractThe dependence structure among each risk factors has been an important topic for researches ...
AbstractThe problem of dependency between two random variables has been studied throughly in the lit...
The problem of dependency between two random variables has been studied throughly in the literature....
This paper studies the general multivariate dependence and tail dependence of a random vector. We an...
Accurately and adequately modeling and analyzing relationships in real random phenomena involving se...
The dependence structure of a d-variate random vector X is a very complex notion which is fully desc...
A fundamental problem in statistics is the estimation of dependence between random variables. While ...
We define a bivariate copula that captures the scale-invariant extent of dependence of a single rand...
<p>The paper presents a new copula based method for measuring dependence between random variables. O...
The paper presents a new copula based method for measuring dependence between random variables. Our ...
Restricted until 15 Feb. 2009.A construction of multivariate distribution functions that allows for ...
In this paper we extend the standard approach of correlation structure analysis in order to reduce t...
The paper is devoted to the multivariate measures of dependence. In contrast to the classical approa...
AbstractA new family of conditional-dependence measures based on Spearman's rho is introduced. The c...
We proposed a new statistical dependency measure called Copula Dependency Coefficient(CDC) for two s...
AbstractThe dependence structure among each risk factors has been an important topic for researches ...
AbstractThe problem of dependency between two random variables has been studied throughly in the lit...
The problem of dependency between two random variables has been studied throughly in the literature....
This paper studies the general multivariate dependence and tail dependence of a random vector. We an...
Accurately and adequately modeling and analyzing relationships in real random phenomena involving se...
The dependence structure of a d-variate random vector X is a very complex notion which is fully desc...
A fundamental problem in statistics is the estimation of dependence between random variables. While ...
We define a bivariate copula that captures the scale-invariant extent of dependence of a single rand...
<p>The paper presents a new copula based method for measuring dependence between random variables. O...
The paper presents a new copula based method for measuring dependence between random variables. Our ...
Restricted until 15 Feb. 2009.A construction of multivariate distribution functions that allows for ...
In this paper we extend the standard approach of correlation structure analysis in order to reduce t...