Dependence Modelling with Copulas

"Introduction Dependence modeling Early research for multivariate non-Gaussian Copula representation for a multivariate distribution Data examples: scatterplots and semi-correlations Likelihood analysis and model comparisons Copula models versus alternative multivariate models Terminology for multivariate distributions with U(0, 1) margins Copula constructions and properties Basics: Dependence, Tail Behavior, and Asymmetries Multivariate cdfs and their conditional distributions Laplace transforms Extreme value theory Tail heaviness Probability integral transform Multivariate Gaussian/normal Ell..."
"Dependence Modeling with Copulas covers the substantial advances that have taken place in the field during the last 15 years, including vine copula modeling of high-dimensional data. Vine copula models are constructed from a sequence of bivariate copulas. The book develops generalizations of vine copula models, including common and structured factor models that extend from the Gaussian assumption to copulas. It also discusses other multivariate constructions and parametric copula families that have different tail properties and presents extensive material on dependence and tail properties to assist in copula model selection."@en