With the availability of massive multivariate data comes a need to develop flexible
multivariate distribution classes. The copula approach allows marginal models to be …

This survey reviews the large and growing literature on the use of pair-copula constructions
(PCCs) in financial applications. Using a PCC, multivariate data that exhibit complex …

This paper presents a data-driven methodology for fault detection and diagnosis (FDD) by
integrating the principal component analysis (PCA) with the Bayesian network (BN). Though …

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 …

Compound events (CEs) are multivariate extreme events in which the individual contributing
variables may not be extreme themselves, but their joint–dependent–occurrence causes an …

Regular vine distributions which constitute a flexible class of multivariate dependence
models are discussed. Since multivariate copulae constructed through pair-copula …

The study of copulas and their applications in statistics is a rather modern phenomenon.
Until quite recently, it was difficult to even locate the word “copula” in the statistical literature …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

Building on the work of Bedford, Cooke and Joe, we show how multivariate data, which
exhibit complex patterns of dependence in the tails, can be modelled using a cascade of …

A new graphical model, called a vine, for dependent random variables is introduced. Vines
generalize the Markov trees often used in modelling high-dimensional distributions. They …