Conditions are given under which the empirical copula process associated with a random sample from a bivariate continuous distribution has a smaller asymptotic covariance function than the standard empirical process based on observations from the copula. Illustrations are provided and consequences for inference are outlined. (C) 2010 Elsevier Inc. All rights reserved
We derive the joint distribution of the ranks associated with a given bivariate random sample. Usin...
We define a copula process which describes the dependencies between arbitrarily many random variable...
This paper is concerned with studying the dependence structure between two random variables Y1 and ...
AbstractConditions are given under which the empirical copula process associated with a random sampl...
Genest and Segers (2010) gave conditions under which the empirical copula process associated with a ...
When the copula of the conditional distribution of two random variables given a covariate does not d...
The empirical copula process plays a central role for statistical inference on copulas. The main pur...
Given a sample from a continuous multivariate distribution FF, the uniform random variates generated...
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...
Weak convergence of the empirical copula process is shown to hold under the assumption that the firs...
In this thesis, we are concerned with strong approximations of the empirical copula process, possibl...
Given a sample from a multivariate distribution F, the uniform random variates generated independent...
The modelling of dependence relations between random variables is one of the most widely studied sub...
The asymptotic behavior of the empirical copula constructed from residuals of stochastic volatility ...
We derive the joint distribution of the ranks associated with a given bivariate random sample. Usin...
We define a copula process which describes the dependencies between arbitrarily many random variable...
This paper is concerned with studying the dependence structure between two random variables Y1 and ...
AbstractConditions are given under which the empirical copula process associated with a random sampl...
Genest and Segers (2010) gave conditions under which the empirical copula process associated with a ...
When the copula of the conditional distribution of two random variables given a covariate does not d...
The empirical copula process plays a central role for statistical inference on copulas. The main pur...
Given a sample from a continuous multivariate distribution FF, the uniform random variates generated...
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...
Weak convergence of the empirical copula process is shown to hold under the assumption that the firs...
In this thesis, we are concerned with strong approximations of the empirical copula process, possibl...
Given a sample from a multivariate distribution F, the uniform random variates generated independent...
The modelling of dependence relations between random variables is one of the most widely studied sub...
The asymptotic behavior of the empirical copula constructed from residuals of stochastic volatility ...
We derive the joint distribution of the ranks associated with a given bivariate random sample. Usin...
We define a copula process which describes the dependencies between arbitrarily many random variable...
This paper is concerned with studying the dependence structure between two random variables Y1 and ...
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