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)
- Indranil Ghosh (Creator)
- Institution
- The University of North Carolina Wilmington (UNCW )
- Web Site: http://library.uncw.edu/
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.
Bivariate Kumaraswamy models via modified symmetric FGM copulas: Properties and Applications in Insurance modeling
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Created on 9/23/2017
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Additional Information
- Publication
- https://doi.org/10.3390/jrfm10040019
- Language: English
- Date: 2017
- Keywords
- Bivariate Kumaraswamy distribution; copula based construction; Kendall'stau; dependence structures; application in insurance risk modeling