On rank correlation measures for non-continuous random variables

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On the empirical multilinear copula process for count data (2014, background): “With some additional work (Nešlehová [14]) it can also be shown that the well-known Spearman and Kendall statistics for testing monotone trend (Agresti [1]) can be rewritten in terms of Ĉ n .”
Copula-Based Measures of Multivariate Association (2010, methodology): “Regarding the case of non-continuous marginal distributions, we refer to Vandenhende and Lambert [112], Nešlehová [80, 81], Denuit and Lambert [19], Mesfioui and Tajar [75], Genest and Nešlehová [37] as well as Feidt et al.”
Regression in a copula model for bivariate count data (2010, methodology): “If F1, F2 are continuous, then C is unique; otherwise, there are many possible copulas as emphasized by Genest and Nes̆lehová [9], but all of these coincide on the closure of Range(F1) × Range(F2), where Range(F ) denotes the range of F .”
Copula-Based Models for Multivariate Discrete Response Data (2013, background): “However for discrete data the marginal distributions also play a role on dependence, and τ does not attain the boundary values of ±1, because the probability of ties Pt = 1− (Pc+Pd) is positive; see [5, 33, 38].”