Comparison of three semiparametric methods for estimating dependence parameters in copula models

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FAST LARGE-SAMPLE GOODNESS-OF-FIT TESTS FOR COPULAS (2011, unknown): “As demonstrated in Kojadinovic and Yan (2010b), ignoring the ties, by using for instance mid-ranks in the computation of the pseudo-observations, may affect the results qualitatively.”
Modeling Dependence with C- and D-Vine Copulas: The R Package CDVine (2013, methodology): “For MLE standard errors are based on inversion of the Hessian matrix, while for inversion of Kendall’s τ they are obtained as described in Kojadinovic and Yan (2010a).”
A goodness-of-fit test for bivariate extreme-value copulas (2011, methodology): “Before commenting on the results, note that for copulas in Groups I and II, the realvalued dependence parameter of each data set was estimated by inversion of Kendall’s tau; its implementation relied on the numerical approximation technique of Kojadinovic and Yan [26].”
ESTIMATORS BASED ON KENDALL'S TAU IN MULTIVARIATE COPULA MODELS (2011, result): “This is in agreement with the simulation study conducted by Kojadinovic & Yan (2010), in which θ ,n outperformed T d ,n for all copula families considered therein.”
Bivariate Flood Frequency Analysis of Upper Godavari River Flows Using Archimedean Copulas (2012, methodology): “However, in recent studies, it was noted that for the asymptotic relative efficiency point of view with finite samples, the MPL estimation method is more efficient (Kojadinovic and Yan 2010).”