Membership testing for Bernoulli and tail-dependence matrices

DOI10.1016/j.jmva.2018.07.014zbMath1420.62254OpenAlexW2787656584MaRDI QIDQ1795588

Matthias Scherer, Ralf Werner, Jonas Schwinn, Daniel Krause

Publication date: 16 October 2018

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2018.07.014

zbMATH Keywords

column generationbinary quadratic programmingBernoulli-compatible matrixtail-dependence matrix

Mathematics Subject Classification ID

Hypothesis testing in multivariate analysis (62H15) Measures of association (correlation, canonical correlation, etc.) (62H20) Special polytopes (linear programming, centrally symmetric, etc.) (52B12) Special problems of linear programming (transportation, multi-index, data envelopment analysis, etc.) (90C08)

Related Items

\(t\)-copula from the viewpoint of tail dependence matrices, On tail dependence matrices. The realization problem for parametric families, Tail-dependence, exceedance sets, and metric embeddings, Geometry of discrete copulas

Cites Work

• Bernoulli and tail-dependence compatibility
• The unconstrained binary quadratic programming problem: a survey
• Some representations of the multivariate Bernoulli and binomial distributions
• The Boolean quadratic polytope: Some characteristics, facets and relatives
• Correlation polytopes: Their geometry and complexity
• The hypermetric cone is polyhedral
• A bound on the \(k\)-gonality of facets of the hypermetric cone and related complexity problems
• Gap inequalities for the cut polytope
• A short proof that the extension complexity of the correlation polytope grows exponentially
• The realization problem for tail correlation functions
• A novel feasible discretization method for linear semi-infinite programming applied to basket option pricing
• Generating Spike Trains with Specified Correlation Coefficients
• A family of multivariate binary distributions for simulating correlated binary variables with specified marginal means and correlations
• A comparison of methods for simulating correlated binary variables with specified marginal means and correlations
• Uniform and minimal {0,1} – cpmatrices
• Range of correlation matrices for dependent Bernoulli random variables
• Geometry of cuts and metrics