An empirical central limit theorem with applications to copulas under weak dependence View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2008-07-01

AUTHORS

Paul Doukhan, Jean-David Fermanian, Gabriel Lang

ABSTRACT

We state a multidimensional Functional Central Limit Theorem for weakly dependent random vectors. We apply this result to copulas. We get the weak convergence of the empirical copula process and of its smoothed version. The finite dimensional convergence of smoothed copula densities is also proved. A new definition and the theoretical analysis of conditional copulas and their empirical counterparts are provided. More... »

PAGES

65-87

References to SciGraph publications

  • 1996. Weak Convergence and Empirical Processes, With Applications to Statistics in NONE
  • 1987. Seminar on Empirical Processes in NONE
  • 2006-01-01. A LARCH(∞) Vector Valued Process in DEPENDENCE IN PROBABILITY AND STATISTICS
  • 1994. Mixing, Properties and Examples in NONE
  • 2002. Weak Dependence: Models and Applications in EMPIRICAL PROCESS TECHNIQUES FOR DEPENDENT DATA
  • 2002-05. Rates in the Empirical Central Limit Theorem for Stationary Weakly Dependent Random Fields in STATISTICAL INFERENCE FOR STOCHASTIC PROCESSES
  • 1999. An Introduction to Copulas in NONE
  • 1981-12. Central Limit Theorems for dependent variables. I in PROBABILITY THEORY AND RELATED FIELDS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11203-008-9026-3

    DOI

    http://dx.doi.org/10.1007/s11203-008-9026-3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1001888303


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