A decomposition of copulas and its use

Engin A. Sungur, Peh Ng

    Research output: Contribution to journalArticlepeer-review

    2 Scopus citations


    In this article, we create a decomposition that represents and describes the depen-dence structure between two variables. Since copulas provide a deep understanding of the dependence structure by eliminating the effects of the marginals, they play a key role in this study. We define a discretized copula density matrix and decompose it into a set of permutation matrices by using the Birkhoff-von Neumann theorem. This decomposition provides a way to effectively apply the concepts of copulas to solve problems in multivariate statistical data analysis.

    Original languageEnglish (US)
    Pages (from-to)2269-2282
    Number of pages14
    JournalCommunications in Statistics - Theory and Methods
    Issue number12
    StatePublished - Dec 1 2005


    • Birkhoff-von Neumann theorem
    • Copulas
    • Discretized copulas
    • Doubly stochastic matrix
    • Gini's measure
    • Permutation matrix
    • Prior probability

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