Multivariate Survival Modelling: A Unified Approach with Copulas

By Pierre Georges, Arnaud Lamy, Emeric Nicolas, Guillaume Quibel, Thierry Roncalli. 2001

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From Dependence to Causation (2016, background): “, 2007), multivariate survival modeling (Georges et al., 2001), spatial statistics, civil engineering, and random vector generation (Jaworski et al.”
Copula Modeling: An Introduction for Practitioners (2007, background): “Georges et al. (2001) provide a review of copula applications to multivariate survival analysis.”
Modelling the joint distribution of competing risks survival times using copula functions (2007, background): “In order to see how the survival copula is expressed through its corresponding copula, we refer to Nelsen (1999) and Georges et al. (2001). The survival copula, C êê , in (8) can be expressed through its copula, C , derived on the basis of specific distributions F1, .”
Empirical estimation of tail dependence using copulas: application to Asian markets (2005, background): “Moreover, a survival copula and its associated copula have the same Kendall’s tau, see Georges et al. (2001) for details.”