Sampling algorithms for generating joint uniform distributions using the Vine-Copula method

DOI10.1016/j.csda.2006.11.043zbMath1161.62363OpenAlexW2158700710WikidataQ56865713 ScholiaQ56865713MaRDI QIDQ1019919

Dorota Kurowicka, Roger M. Cooke

Publication date: 29 May 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2006.11.043

zbMATH Keywords

samplingvinesjoint distributiondependence modelingjoint uniform

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10)

Related Items (5)

Quasi-random numbers for copula models ⋮ Specification of informative prior distributions for multinomial models using vine copulas ⋮ Pair-copula models for analyzing family data ⋮ Pair-copula constructions of multiple dependence ⋮ Economic and financial risk factors, copula dependence and risk sensitivity of large multi-asset class portfolios

Cites Work

• A distribution-free approach to inducing rank correlation among input variables
• Completion problem with partial correlation vines
• On the simultaneous associativity of F(x,y) and x+y-F(x,y)
• An introduction to copulas. Properties and applications
• A parameterization of positive definite matrices in terms of partial correlation vines
• Vines -- a new graphical model for dependent random variables.
• Statistical Inference Procedures for Bivariate Archimedean Copulas
• Minimally informative distributions with given rank correlation for use in uncertainty analysis
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