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
Consider a nonparametric regression model Y = m(X)+✏, where m is an unknown regression function, Y i...
AbstractThe manner in which two random variables influence one another often depends on covariates. ...
Three simple and explicit procedures for testing the independence of two multi-dimensional random va...
summary:We introduce new estimates and tests of independence in copula models with unknown margins u...
Copulas offer a convenient way of modelling multivariate observations and capturing the intrinsic de...
At the heart of the copula methodology in statistics is the idea of separating marginal distribution...
summary:In this paper we analyze some properties of the empirical diagonal and we obtain its exact d...
At the heart of the copula methodology in statistics is the idea of separating marginal distribution...
Tests of multivariate independence may rely on asymptotically independent Cramér-von Mises statistic...
summary:In the paper we investigate properties of maximum pseudo-likelihood estimators for the copul...
This thesis investigates three topics in theoretical econometrics: goodness-of-fit tests for copulas...
New statistics are proposed for testing the hypothesis that two non-continuous random variables are ...
Universite ́ catholique de Louvain and Tilburg University At the heart of the copula methodology in ...
The copula-based modeling of multivariate distributions with continuous margins is presented as a su...
A common stochastic restriction in econometric models separable in the latent variables is the assum...
Consider a nonparametric regression model Y = m(X)+✏, where m is an unknown regression function, Y i...
AbstractThe manner in which two random variables influence one another often depends on covariates. ...
Three simple and explicit procedures for testing the independence of two multi-dimensional random va...
summary:We introduce new estimates and tests of independence in copula models with unknown margins u...
Copulas offer a convenient way of modelling multivariate observations and capturing the intrinsic de...
At the heart of the copula methodology in statistics is the idea of separating marginal distribution...
summary:In this paper we analyze some properties of the empirical diagonal and we obtain its exact d...
At the heart of the copula methodology in statistics is the idea of separating marginal distribution...
Tests of multivariate independence may rely on asymptotically independent Cramér-von Mises statistic...
summary:In the paper we investigate properties of maximum pseudo-likelihood estimators for the copul...
This thesis investigates three topics in theoretical econometrics: goodness-of-fit tests for copulas...
New statistics are proposed for testing the hypothesis that two non-continuous random variables are ...
Universite ́ catholique de Louvain and Tilburg University At the heart of the copula methodology in ...
The copula-based modeling of multivariate distributions with continuous margins is presented as a su...
A common stochastic restriction in econometric models separable in the latent variables is the assum...
Consider a nonparametric regression model Y = m(X)+✏, where m is an unknown regression function, Y i...
AbstractThe manner in which two random variables influence one another often depends on covariates. ...
Three simple and explicit procedures for testing the independence of two multi-dimensional random va...