Genest and Segers (2010) gave conditions under which the empirical copula process associated with a random sample from a bivariate continuous distribution has a smaller asymptotic covariance than the standard empirical process based on a random sample from the underlying copula. An extension of this result to the multivariate case is provided
We consider a nonparametric method to estimate copulas, i.e. functions linking joint distributions t...
For multivariate distributions in the domain of attraction of a max-stable distribution, the tail co...
We study the weak convergence of conditional empirical copula processes indexed by general families ...
Conditions are given under which the empirical copula process associated with a random sample from a...
AbstractConditions are given under which the empirical copula process associated with a random sampl...
When the copula of the conditional distribution of two random variables given a covariate does not d...
Given a sample from a continuous multivariate distribution FF, the uniform random variates generated...
In this paper, we study the asymptotic behavior of the sequential empirical process and the sequent...
The empirical copula process plays a central role for statistical inference on copulas. The main pur...
Given a sample from a multivariate distribution F, the uniform random variates generated independent...
In this thesis, we are concerned with strong approximations of the empirical copula process, possibl...
We define a copula process which describes the dependencies between arbitrarily many random variable...
We define a copula process which describes the dependencies between arbitrarily many random variable...
The modelling of dependence relations between random variables is one of the most widely studied sub...
Weak convergence of the empirical copula process is shown to hold under the assumption that the firs...
We consider a nonparametric method to estimate copulas, i.e. functions linking joint distributions t...
For multivariate distributions in the domain of attraction of a max-stable distribution, the tail co...
We study the weak convergence of conditional empirical copula processes indexed by general families ...
Conditions are given under which the empirical copula process associated with a random sample from a...
AbstractConditions are given under which the empirical copula process associated with a random sampl...
When the copula of the conditional distribution of two random variables given a covariate does not d...
Given a sample from a continuous multivariate distribution FF, the uniform random variates generated...
In this paper, we study the asymptotic behavior of the sequential empirical process and the sequent...
The empirical copula process plays a central role for statistical inference on copulas. The main pur...
Given a sample from a multivariate distribution F, the uniform random variates generated independent...
In this thesis, we are concerned with strong approximations of the empirical copula process, possibl...
We define a copula process which describes the dependencies between arbitrarily many random variable...
We define a copula process which describes the dependencies between arbitrarily many random variable...
The modelling of dependence relations between random variables is one of the most widely studied sub...
Weak convergence of the empirical copula process is shown to hold under the assumption that the firs...
We consider a nonparametric method to estimate copulas, i.e. functions linking joint distributions t...
For multivariate distributions in the domain of attraction of a max-stable distribution, the tail co...
We study the weak convergence of conditional empirical copula processes indexed by general families ...