Measure of dependence is a particular way of looking at the association between random variables, and one way to capture stochastic dependence is through the use of copula. In this study, a Rushendorf Method was applied to a bivariate function to obtain a copula through the use of a special case of Rodriguez-Lallena and Ubeda-Flores (RLUF) copula. Properties of the RLUF copula such as the density, measures of dependence, and lower and upper tail dependence were studied. In particular, measures of dependence such as Spearman’s rho, Kendall’s tau and Blomqvist’s beta of RLUF copula are given. Moreover, the Root-Mean-Square Error (RMSE), Sum-Square Error (SSE), Mean Absolute Error (MAE), Mean Square Error (MSE), Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used in deriving the best joint distribution between monthly precipitation and temperature in the Philippines from 1974 to 2013. The results showed that considering the monthly precipitation and temperature datas, RLUF copula outperformed the other existing bivariate copulas such as Ali-Mikhail-Haq (AHM), Farlie-Gumbel-Morgenstern (FGM), and Clayton copulas.
Pizon, Marvin G. and Paluga, Rolando N.
(R1510) A Special Case of Rodriguez-Lallena and Ubeda-Flores Copula Based on Ruschendorf Method,
Applications and Applied Mathematics: An International Journal (AAM), Vol. 17,
1, Article 2.
Available at: https://digitalcommons.pvamu.edu/aam/vol17/iss1/2