Submitted:
23 September 2017
Posted:
25 September 2017
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Abstract
A copula is a useful tool for constructing bivariate and/or multivariate distributions. In this article, we consider a new modi fied class of (Farlie-Gumbel-Morgenstern) FGM bivariate copula for constructing several di erent bivariate Kumaraswamy type copulas and discuss their structural properties, including dependence structures. It is established that construction of bivariate distributions by this method allows for greater flexibility in the values of Spearman's correlation coefficient rho, and Kendall's tau . For illustrative purposes, one representative data set is utilized to exhibit the applicability of these proposed bivariate copula models.
Keywords:
Bivariate Kumaraswamy distribution
; copula based construction
; Kendall'stau
; dependence structures
; application in insurance risk modeling
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