In this paper the interest is to estimate the dependence between two variables conditionally upon a covariate, through copula modelling. In recent literature nonparametric estimators for conditional copula functions in case of a univariate covariate have been proposed. The aim of this paper is to nonparametrically estimate a conditional copula when the covariate takes on values in more complex spaces. We consider multivariate covariates and functional covariates. We establish weak convergence, and bias and variance properties of the proposed nonparametric estimators. We also briefly discuss nonparametric estimation of conditional association measures such as a conditional Kendall’s tau. The case of functional covariates is of particular int...
We consider a new approach in quantile regression modeling based on the copula function that defines...
Conditional copulas are flexible statistical tools that couple joint conditional and marginal condit...
We consider a new approach in quantile regression modeling based on the copula function that defines...
This paper is concerned with inference about the dependence or association between two random variab...
The primary aim of this thesis is the elucidation of covariate effects on the dependence structure o...
The primary aim of this thesis is the elucidation of covariate effects on the dependence structure o...
This paper is concerned with studying the dependence structure between two random variables Y1 and ...
International audienceThe tail copula is widely used to describe the dependence in the tail of multi...
© 2017 Elsevier Inc. We consider copula modeling of the dependence between two or more random variab...
AbstractThe manner in which two random variables influence one another often depends on covariates. ...
AbstractThe manner in which two random variables influence one another often depends on covariates. ...
When the copula of the conditional distribution of two random variables given a covariate does not d...
A common assumption in pair-copula constructions is that the copula of the conditional distribution ...
© 2008 Australian Statistical Publishing Association Inc.Not only are copula functions joint distrib...
This paper concerns the dependence structure of a random pair (Y1,Y2) conditionally upon a covariate...
We consider a new approach in quantile regression modeling based on the copula function that defines...
Conditional copulas are flexible statistical tools that couple joint conditional and marginal condit...
We consider a new approach in quantile regression modeling based on the copula function that defines...
This paper is concerned with inference about the dependence or association between two random variab...
The primary aim of this thesis is the elucidation of covariate effects on the dependence structure o...
The primary aim of this thesis is the elucidation of covariate effects on the dependence structure o...
This paper is concerned with studying the dependence structure between two random variables Y1 and ...
International audienceThe tail copula is widely used to describe the dependence in the tail of multi...
© 2017 Elsevier Inc. We consider copula modeling of the dependence between two or more random variab...
AbstractThe manner in which two random variables influence one another often depends on covariates. ...
AbstractThe manner in which two random variables influence one another often depends on covariates. ...
When the copula of the conditional distribution of two random variables given a covariate does not d...
A common assumption in pair-copula constructions is that the copula of the conditional distribution ...
© 2008 Australian Statistical Publishing Association Inc.Not only are copula functions joint distrib...
This paper concerns the dependence structure of a random pair (Y1,Y2) conditionally upon a covariate...
We consider a new approach in quantile regression modeling based on the copula function that defines...
Conditional copulas are flexible statistical tools that couple joint conditional and marginal condit...
We consider a new approach in quantile regression modeling based on the copula function that defines...