Measure of association is a broad term that denotes the class of all the measures that have been constructed with the aim of quantifying specific relationships between two or more variables. Measures of association based on copulas are invariant with respect to the univariate marginal distributions. These measures are able to capture positive as well as negative association and they do not alter the value in case of strictly increasing transformations of the variables
A continuous random vector (X,Y) uniquely determines a copula C: [0,1]2 → [0,1] such that when the d...
Nonparametric estimation of copula-based measures of multivariate association in a continuous random...
We apply the inversion method of estimation, with several combinations of two among the four most po...
We survey the measures of association that are based on bivariate copulas. Almost no proof will be r...
The dependence structure of a d-variate random vector X is a very complex notion which is fully desc...
Measures of association are suggested between two random vectors. The measures are copula-based and ...
For a pair (Y1,Y2) of random variables there exist several measures of association that characterize...
Measures of association are suggested between two random vectors. The measures are copula-based and ...
Building upon earlier work in which axioms were formulated for multivariate measures of concordance,...
Building upon earlier work in which axioms were formulated for multivariate measures of concordance,...
This contribution provides an overview of the commonly used measures of association for categorical ...
A number of X2 based nonparametric tests are used to determine the level of statistical significance...
Celem artykułu jest przedstawienie miar zależności opartych na kopulach. Omówione zostały następując...
Copulas are real functions representing the dependence structure of the distribution of a random vec...
We call a measure of concordance [kappa] of an ordered pair (X,Y) of two continuous random variables...
A continuous random vector (X,Y) uniquely determines a copula C: [0,1]2 → [0,1] such that when the d...
Nonparametric estimation of copula-based measures of multivariate association in a continuous random...
We apply the inversion method of estimation, with several combinations of two among the four most po...
We survey the measures of association that are based on bivariate copulas. Almost no proof will be r...
The dependence structure of a d-variate random vector X is a very complex notion which is fully desc...
Measures of association are suggested between two random vectors. The measures are copula-based and ...
For a pair (Y1,Y2) of random variables there exist several measures of association that characterize...
Measures of association are suggested between two random vectors. The measures are copula-based and ...
Building upon earlier work in which axioms were formulated for multivariate measures of concordance,...
Building upon earlier work in which axioms were formulated for multivariate measures of concordance,...
This contribution provides an overview of the commonly used measures of association for categorical ...
A number of X2 based nonparametric tests are used to determine the level of statistical significance...
Celem artykułu jest przedstawienie miar zależności opartych na kopulach. Omówione zostały następując...
Copulas are real functions representing the dependence structure of the distribution of a random vec...
We call a measure of concordance [kappa] of an ordered pair (X,Y) of two continuous random variables...
A continuous random vector (X,Y) uniquely determines a copula C: [0,1]2 → [0,1] such that when the d...
Nonparametric estimation of copula-based measures of multivariate association in a continuous random...
We apply the inversion method of estimation, with several combinations of two among the four most po...