An Introduction to Copulas

By Roger B. Nelsen. 1998

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Citations in the corpus

Causal Inference through a Witness Protection Program (2014, background): “One general model family in which vanishing partial correlations are closely connected to independence is the Gaussian copula (Elidan, 2013; Nelsen, 2007).”
Triangular norms as ordinal sums of semigroups in the sense of A. H. Clifford (2002, background): “Triangular norms also play an important role as generalized conjunctions in many-valued logics [11], [13], [19] and fuzzy logics [25], [38], but also in decision making [9], [12], statistics [ 24 ] as well as in the theories of non-additive measures [20], [27], [34], [36] and cooperative games [1].”
Partial Distributional Policy Effects (2011, unknown): “7 in Nelsen (2006). The extension of his result to the general multivariate case is immediate.”
Estimating the tail-dependence coefficient: Properties and pitfalls (2005, background): “Refer to Nelsen (1999) or Joe (1997) for more information on copulas.”
Tail Risk of Multivariate Regular Variation (2011, methodology): “developing versatile parametric dependence models [13, 22 ].”
The ruin probability of the renewal model with constant interest force and negatively dependent heavy-tailed claims (2007, background): “Let (X1 ,X 2) be a random vector with continuous marginal distributions F1 and F2. Then the dependence structure of X1 and X2 is completely determined by a bivariate copula function C(u, v) by the Sklar’s representation (see Nelsen [ 6 ] or Joe [7]), and the joint distribution function of X1 and X2 ”
Evaluation of scenario-generation methods for stochastic programming (2007, background): “In such a case, we may try to match also higher comoments, or use a copula ([28, 4]), if we have the necessary data and a scenario-generation method that can work with these properties.”