Generalized bivariate copulas and their properties

Jong-Min Kim, Engin A. Sungur, Taeryon Choi, Tae Young Heo

    Research output: Contribution to journalArticlepeer-review

    17 Scopus citations

    Abstract

    Copulas are useful devices to explain the dependence structure among variables by eliminating the influence of marginals. In this paper, we propose a new class of bivariate copulas to quantify dependency and incorporate it into various iterated copula families. We investigate properties of the new class of bivariate copulas and derive the measure of association, such as Spearman's ρ, Kendall's τ, and the regression function for the new class. We also provide the concept of directional dependence in bivariate regression setting by using copulas.

    Original languageEnglish (US)
    Pages (from-to)127-136
    Number of pages10
    JournalModel Assisted Statistics and Applications
    Volume6
    Issue number2
    DOIs
    StatePublished - Jun 3 2011

    Keywords

    • Bivariate copulas
    • Directional dependence
    • Farlie-Gumbel-Morgenstern copula
    • Kendall's τ
    • Marginal distribution
    • Regression function
    • Spearman's ρ

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