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On a bivariate copula for modeling negative dependence: application to New York air quality data

Author

Listed:
  • Shyamal Ghosh

    (Indian Institute of Information Technology Guwahati)

  • Prajamitra Bhuyan

    (Queen Mary University of London
    The Alan Turing Institute)

  • Maxim Finkelstein

    (University of the Free State
    University of Strathclyde)

Abstract

In many practical scenarios, including finance, environmental sciences, system reliability, etc., it is often of interest to study the various notion of negative dependence among the observed variables. A new bivariate copula is proposed for modeling negative dependence between two random variables that complies with most of the popular notions of negative dependence reported in the literature. Specifically, the Spearman’s rho and the Kendall’s tau for the proposed copula have a simple one-parameter form with negative values in the full range. Some important ordering properties comparing the strength of negative dependence with respect to the parameter involved are considered. Simple examples of the corresponding bivariate distributions with popular marginals are presented. Application of the proposed copula is illustrated using a real data set on air quality in the New York City, USA.

Suggested Citation

  • Shyamal Ghosh & Prajamitra Bhuyan & Maxim Finkelstein, 2022. "On a bivariate copula for modeling negative dependence: application to New York air quality data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(5), pages 1329-1353, December.
    • Handle: RePEc:spr:stmapp:v:31:y:2022:i:5:d:10.1007_s10260-022-00636-3
    DOI: 10.1007/s10260-022-00636-3
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