Multivariate dependence modeling based on comonotonic factors
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Publication:512029
DOI10.1016/j.jmva.2017.01.008zbMath1356.62076OpenAlexW2573623087MaRDI QIDQ512029
Publication date: 23 February 2017
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jmva.2017.01.008
Multivariate analysis (62H99) Factor analysis and principal components; correspondence analysis (62H25)
Related Items (2)
Detection of block-exchangeable structure in large-scale correlation matrices ⋮ Assessing bivariate tail non-exchangeable dependence
Uses Software
Cites Work
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- Pair-copula constructions of multiple dependence
- Factor copula models for multivariate data
- A generalized beta copula with applications in modeling multivariate long-tailed data
- Tail order and intermediate tail dependence of multivariate copulas
- Multivariate Archimedean copulas, \(d\)-monotone functions and \(\ell _{1}\)-norm symmetric distributions
- From Archimedean to Liouville copulas
- The concept of comonotonicity in actuarial science and finance: theory.
- Multivariate models for dependent clusters of variables with conditional independence given aggregation variables
- Tail negative dependence and its applications for aggregate loss modeling
- Probability density decomposition for conditionally dependent random variables modeled by vines
- Structured factor copula models: theory, inference and computation
- Thetcopula with multiple parameters of degrees of freedom: bivariate characteristics and application to risk management
- Some inequalities for the expectation of a product of functions of a random variable and for the multivariate distribution function at a random point
- Dependence Modeling with Copulas
- Algorithms for Generating Discrete Random Variables with a Given Generating Function or a Given Moment Sequence
- Simulating Copulas
- Truncated regular vines in high dimensions with application to financial data
- Copules archimédiennes et families de lois bidimensionnelles dont les marges sont données
- Pair Copula Constructions for Multivariate Discrete Data
- Families of Multivariate Distributions
- Hierarchical Kendall copulas: Properties and inference
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