Bivariate Kumaraswamy models via modified symmetric FGM copulas: Properties and Applications in Insurance modeling

UNCW Author/Contributor (non-UNCW co-authors, if there are any, appear on document)
Ghosh, Indranil (Creator)
The University of North Carolina Wilmington (UNCW )
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Abstract: A copula is a useful tool for constructing bivariate and/or multivariate dis- tributions. In this article, we consider a new modified class of (Farlie-Gumbel- Morgenstern) FGM bivariate copula for constructing several different bivariate Ku- maraswamy type copulas and discuss their structural properties, including depen- dence structures. It is established that construction of bivariate distributions by this method allows for greater flexibility in the values of Spearman’s correlation coefficient, ? and Kendall’s t. For illustrative purposes, one representative data set is utilized to exhibit the applicability of these proposed bivariate copula models.

Additional Information

Language: English
Date: 2017
Bivariate Kumaraswamy distribution; copula based construction; Kendall'stau; dependence structures; application in insurance risk modeling

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