summary:We introduce new estimates and tests of independence in copula models with unknown margins using $\phi$-divergences and the duality technique. The asymptotic laws of the estimates and the test statistics are established both when the parameter is an interior or a boundary value of the parameter space. Simulation results show that the choice of $\chi^2$-divergence has good properties in terms of efficiency-robustness
Conditions for the asymptotic semiparametric efficiency of an omnibus estimator of dependence parame...
We present a family of smooth tests for the goodness of fit of semiparametric multivariate copula mo...
New statistics are proposed for testing the hypothesis that arbitrary random variables are mutually ...
summary:We introduce new estimates and tests of independence in copula models with unknown margins u...
At the heart of the copula methodology in statistics is the idea of separating marginal distribution...
At the heart of the copula methodology in statistics is the idea of separating marginal distribution...
It is well known that, given observable data for a competing risk problem, there is always an indepe...
Tests of multivariate independence may rely on asymptotically independent Cramér-von Mises statistic...
Universite ́ catholique de Louvain and Tilburg University At the heart of the copula methodology in ...
New statistics are proposed for testing the hypothesis that two non-continuous random variables are ...
In the present paper, we are mainly concerned with the statistical inference for the functional of n...
summary:In this paper we analyze some properties of the empirical diagonal and we obtain its exact d...
In this study, we estimate the Kendall distribution function (K(t)) for Archimedean copula family us...
This paper is concerned with studying the dependence structure between two random variables Y1 and ...
Consider semiparametric bivariate copula models in which the family of copula functions is parametri...
Conditions for the asymptotic semiparametric efficiency of an omnibus estimator of dependence parame...
We present a family of smooth tests for the goodness of fit of semiparametric multivariate copula mo...
New statistics are proposed for testing the hypothesis that arbitrary random variables are mutually ...
summary:We introduce new estimates and tests of independence in copula models with unknown margins u...
At the heart of the copula methodology in statistics is the idea of separating marginal distribution...
At the heart of the copula methodology in statistics is the idea of separating marginal distribution...
It is well known that, given observable data for a competing risk problem, there is always an indepe...
Tests of multivariate independence may rely on asymptotically independent Cramér-von Mises statistic...
Universite ́ catholique de Louvain and Tilburg University At the heart of the copula methodology in ...
New statistics are proposed for testing the hypothesis that two non-continuous random variables are ...
In the present paper, we are mainly concerned with the statistical inference for the functional of n...
summary:In this paper we analyze some properties of the empirical diagonal and we obtain its exact d...
In this study, we estimate the Kendall distribution function (K(t)) for Archimedean copula family us...
This paper is concerned with studying the dependence structure between two random variables Y1 and ...
Consider semiparametric bivariate copula models in which the family of copula functions is parametri...
Conditions for the asymptotic semiparametric efficiency of an omnibus estimator of dependence parame...
We present a family of smooth tests for the goodness of fit of semiparametric multivariate copula mo...
New statistics are proposed for testing the hypothesis that arbitrary random variables are mutually ...