Results 11 to 20 of about 65 (65)
Inference for copula modeling of discrete data: a cautionary tale and some facts
Dependence Modeling, 2017 In this note, we elucidate some of the mathematical, statistical and epistemological issues involved in using copulas to model discrete data. We contrast the possible use of (nonparametric) copula methods versus the problematic use of parametric copula ...
Faugeras Olivier P.
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Multivariate medial correlation with applications
Dependence Modeling, 2020 We define a multivariate medial correlation coefficient that extends the probabilistic interpretation and properties of Blomqvist’s β coefficient, incorporates multivariate marginal dependencies and it preserves a partial ordering stronger than ...
Ferreira Helena, Ferreira Marta
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Copula modeling for discrete random vectors
Dependence Modeling, 2020 Copulas have now become ubiquitous statistical tools for describing, analysing and modelling dependence between random variables. Sklar’s theorem, “the fundamental theorem of copulas”, makes a clear distinction between the continuous case and the ...
Geenens Gery
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Portfolio selection based on graphs: Does it align with Markowitz-optimal portfolios?
Dependence Modeling, 2018 Some empirical studies suggest that the computation of certain graph structures from a (large) historical correlation matrix can be helpful in portfolio selection.
Hüttner Amelie +2 more
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Optimizing effective numbers of tests by vine copula modeling
Dependence Modeling, 2020 In the multiple testing context, we utilize vine copulae for optimizing the effective number of tests. It is well known that for the calibration of multiple tests for control of the family-wise error rate the dependencies between the marginal tests are ...
Steffen Nico, Dickhaus Thorsten
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New copulas based on general partitions-of-unity and their applications to risk management (part II)
Dependence Modeling, 2017 We present a constructive and self-contained approach to data driven infinite partition-of-unity copulas that were recently introduced in the literature.
Pfeifer Dietmar +2 more
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Dependence Modeling, 2023 Working with shuffles, we establish a close link between Kendall’s τ\tau , the so-called length measure, and the surface area of bivariate copulas and derive some consequences.
Sánchez Juan Fernández +1 more
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Dependence of Stock Returns in Bull and Bear Markets
Dependence Modeling, 2013 Despite of its many shortcomings, Pearson’s rho is often used as an association measure for stock returns. A conditional version of Spearman’s rho is suggested as an alternative measure of association. This approach is purely nonparametric and avoids any
Dobric Jadran +2 more
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A topological proof of Sklar’s theorem in arbitrary dimensions
Dependence Modeling, 2022 Copulas are appealing tools in multivariate probability theory and statistics. Nevertheless, the transfer of this concept to infinite dimensions entails some nontrivial topological and functional analytic issues, making a deeper theoretical understanding
Benth Fred Espen +2 more
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A nonparametric test for comparing survival functions based on restricted distance correlation
Dependence Modeling, 2023 In this article, we propose an omnibus test for comparing two survival functions under non-proportional hazards. The test statistic is based on a product-limit estimate of the restricted distance correlation, which is closely related to the L2{L}_{2 ...
Zhang Qingyang
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