Estimating scale-invariant directed dependence of bivariate distributions
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Publication:85343
DOI10.1016/j.csda.2020.107058OpenAlexW3048149558MaRDI QIDQ85343
Wolfgang Trutschnig, Florian Griessenberger, Robert R. Junker, Robert R. Junker, Florian Griessenberger, Wolfgang Trutschnig
Publication date: January 2021
Published in: Computational Statistics & Data Analysis, Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2020.107058
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Uses Software
Cites Work
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- Measuring and testing dependence by correlation of distances
- On a strong metric on the space of copulas and its induced dependence measure
- Detecting Novel Associations in Large Data Sets
- Convergence results for patchwork copulas
- On the empirical multilinear copula process for count data
- Large sample behavior of the Bernstein copula estimator
- The empirical beta copula
- An introduction to copulas.
- Extremes of nonexchangeability
- Conditioning-based metrics on the space of multivariate copulas and their interrelation with uniform and levelwise convergence and iterated function systems
- On nonparametric measures of dependence for random variables
- Strong approximation of copulas
- On approximation of copulas
- An empirical study of the maximal and total information coefficients and leading measures of dependence
- Maximum asymmetry of copulas revisited
- Process-driven direction-dependent asymmetry: identification and quantification of directional dependence in spatial fields
- Review on statistical methods for gene network reconstruction using expression data
- Measuring dependence powerfully and equitably
- An informational measure of correlation
- On measures of dependence
- THE BERNSTEIN COPULA AND ITS APPLICATIONS TO MODELING AND APPROXIMATIONS OF MULTIVARIATE DISTRIBUTIONS
- A Copula‐Based Non‐parametric Measure of Regression Dependence
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