Sentence examples for multivariate copulas is from inspiring English sources

The parametric estimation of multivariate copulas is a method that could be applied [20].

Comparisons of the environmental contours using C-vines with those obtained from standard multivariate copulas are also discussed.

Three methods of creating multivariate copulas were applied and the results were compared using (1) Kendall's measure; (2) empirical multivariate distributions; and (3) simulations.

The multivariate normal copula is defined as with density Thus for given marginal distribution functions F1, …, F d and their densities f1, …, f d, the joint distribution function for the multivariate normal copula with these given margins is with density For the distribution in (6), any lower dimensional joint distributions have the same form.

Since copulas link univariate marginals to their full multivariate distribution, an important feature of copulas is that any choice of marginal distributions can be used.

A multivariate copula, which is established based on bivariate copulas and conditional probability distributions, is employed to describe temporal dependences (single site) and spatial dependences (between sites).

In this study, we decide to employ a multivariate copula, which is more applicable in practice; the traditional candidate for modeling dependence is Gaussian copula (for application of this copula in a real bank see Aue and Kalkbrener (2006)).

The approach based on vine copulas is a way to construct multivariate distributions using bivariate copulas as building blocks.

Copulas are multivariate distribution functions with standard uniform univariate margins.

The accuracy of the developed marginal distributions and empirical copulas were verified using multivariate Monte Carlo simulations.

Copulas are functions that join multivariate distribution functions to their one-dimensional marginal distribution functions.