Bernoulli · 2004 · 386 citations · 15 references
Natural SciencesMathematicsEconometricsProbability TheoryStochastic ProcessesLimit TheoremsWeak ConvergenceCumulative Distribution FunctionsEmpirical Distribution FunctionsMarginal DistributionsWeak Convergence Of Stochastic Processes
Weak convergence of the empirical copula process has been established for independent marginals and under distribution‑function restrictions via the functional delta method. We aim to extend these results by proving weak convergence of the empirical copula process in ℓ∞([0,1]²) under minimal copula conditions. The proof employs the functional delta method, which also yields immediate convergence of the bootstrap counterpart. The authors argue that the copula condition is necessary and establish weak convergence of the smoothed empirical copula process.
Weak convergence of the empirical copula process has been established by Deheuvels in the case of independent marginal distributions. Van der Vaart and Wellner utilize the functional delta method to show convergence in ℓ ∞ ([a,b] 2) for some 0<a<b<1, under restrictions on the distribution functions. We extend their results by proving the weak convergence of this process in ℓ ∞ ([0,1] 2) under minimal conditions on the copula function, which coincides with the result obtained by Gaenssler and Stute. It is argued that the condition on the copula function is necessary. The proof uses the functional delta method and, as a consequence, the convergence of the bootstrap counterpart of the empirical copula process follows immediately. In addition, weak convergence of the smoothed empirical copula process is established.
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Multivariate Models and Dependence Concepts.
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