Constraints on concordance measures in bivariate discrete data

By Michel Denuit, Philippe Lambert. 2005

203 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b3e50407a2c76df572c4e218414357700be65ee6

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In Artificial Intelligence

In Management Science and Operations Research

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Citations in the corpus

Analysis of directional dependence using asymmetric copula-based regression models (2014, methodology): “Thus, we obtain a continuous extension of the hyperactive-impulsiveness by using the randomization technique proposed by [36]: given integer-valued Xi, consider a continuous random variable X i = Xi + (Ui − 1) where Ui is uniform on (0, 1) and independent of Xi.”
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].”
A Primer on Copulas for Count Data (2007, methodology): “The normalizations used in Definition 3 are obviously not the only possible ones; see, e.g., Denuit and Lambert (2005) for an alternative.”
Quantitative models for operational risk: Extremes, dependence and aggregation (2006, background): “…nice stochastic interpretations and have to be handled with care because of the non-continuity of the marginals, they cover a wide range of dependence possibilities; see Griffiths et al. (1979), Nešlehová (2004), Pfeifer and Nešlehová (2004) and Denuit and Lambert (2005) for further details.”
Copula-Based Models for Financial Time Series (2009, background): “See Denuit and Lambert (2005) for one such method.”
Regression in a copula model for bivariate count data (2010, background): “Denuit and Lambert [6], Mesfioui and Tajar [21], and Nes̆lehová [25] studied the behavior of Kendall’s tau applied to discrete data.”
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.”
Pair Copula Constructions for Multivariate Discrete Data (2012, methodology): “Consequently, in contrast to the continuous data case, the Kendall’s τ of the data will not be equivalent to the Kendall’s τ of the bivariate copulas (see Denuit and Lambert ( 2005 )) 3 .”
On rank correlation measures for non-continuous random variables (2007, methodology): “A natural approach that overcomes this has also recently been considered by Denuit and Lambert [14] for purely discrete random variables on a subset of N; these authors construct a continuous “extension” of X.”
Multivariate autoregressive modeling of time series count data using copulas (2007, methodology): “We use the continued extension argument proposed by Denuit and Lambert (2005) to overcome these difficulties and apply copulas with discrete marginals.”
On the properties of some nonparametric concordance measures in the discrete case (2005, methodology): “This idea was used recently by Denuit and Lambert [7] to examine bivariate discrete data.”
Multivariate Negative Binomial Models for Insurance Claim Counts (2014, methodology): “The jitter approach was inspired by the pioneer work of Denuit and Lambert (2005), where it has been demonstrated that the concordance-based association measures, such as Kendall’s tau, are preserved when jittering discrete data. The preservation of association also allows us to naturally interpret ”