TY - JOUR
T1 - A new extension of the FGM copula for negative association
AU - Cooray, Kahadawala
N1 - Publisher Copyright:
© 2018, © 2018 Taylor & Francis Group, LLC.
PY - 2019/4/18
Y1 - 2019/4/18
N2 - In this paper we introduced a single parameter, absolutely continuous and radially symmetric bivariate extension of the Farlie-Gumbel-Morgenstern (FGM) family of copulas. Specifically, this extension measures the higher negative dependencies than most FGM extensions available in literature. Closed-form formulas for distribution, quantile, density, conditional distribution, regression, Spearman's rho, Kendall's tau, and Gini's gamma are obtained. In addition, a formula for random variate generations is presented in closed-form to facilitate simulation studies. We conduct both paired and multiple comparisons with Frank, Gaussian, and Plackett copulas to investigate the performance based on Vuong's test. Furthermore, the new copula is compared with Frank, Gaussian, and Plackett copulas using both Kolmogorov-Smirnov and Cramér-von Mises type test statistics. Finally, a bivariate dataset is analyzed to compare and illustrate the flexibility of the new copula for negative dependence.
AB - In this paper we introduced a single parameter, absolutely continuous and radially symmetric bivariate extension of the Farlie-Gumbel-Morgenstern (FGM) family of copulas. Specifically, this extension measures the higher negative dependencies than most FGM extensions available in literature. Closed-form formulas for distribution, quantile, density, conditional distribution, regression, Spearman's rho, Kendall's tau, and Gini's gamma are obtained. In addition, a formula for random variate generations is presented in closed-form to facilitate simulation studies. We conduct both paired and multiple comparisons with Frank, Gaussian, and Plackett copulas to investigate the performance based on Vuong's test. Furthermore, the new copula is compared with Frank, Gaussian, and Plackett copulas using both Kolmogorov-Smirnov and Cramér-von Mises type test statistics. Finally, a bivariate dataset is analyzed to compare and illustrate the flexibility of the new copula for negative dependence.
KW - Coverage probabilities
KW - Kendall's distribution function
KW - goodness-of-fit
KW - maximum likelihood estimation
KW - measures of association
KW - p-value
UR - http://www.scopus.com/inward/record.url?scp=85042937254&partnerID=8YFLogxK
U2 - 10.1080/03610926.2018.1440312
DO - 10.1080/03610926.2018.1440312
M3 - Article
AN - SCOPUS:85042937254
SN - 0361-0926
VL - 48
SP - 1902
EP - 1919
JO - Communications in Statistics - Theory and Methods
JF - Communications in Statistics - Theory and Methods
IS - 8
ER -