This paper discusses the proposals of Li and Thompson (1977), Yen (1986), and Feng (1993) in relation to the development of the Chinese copula and argues that Li and Thompson’s suggestion of a topic mechanism, Yen’s analogical change, and Feng’s phonological pause are unsatisfactory in explaining the development of the copula. It is suggested that Katz’s (1996) cognitive concept of existence in...

We present the Copula Bayesian Network model for representing multivariate continuous distributions, while taking advantage of the relative ease of estimating univariate distributions. Using a novel copula-based reparameterization of a conditional density, joined with a graph that encodes independencies, our model offers great flexibility in modeling high-dimensional densities, while maintainin...

empirical studies show that there is stronger dependency between large losses than large profit in financial market, which undermine the performance of using symmetric distribution for modeling these asymmetric. that is why the assuming normal joint distribution of returns is not suitable because of considering the linier dependence, and can be lead to inappropriate estimate of var. copula theo...

One of the key questions in credit dependence modelling is the specfication of the copula function linking the marginals of default variables. Copulae functions are important because they allow to decouple statistical inference into two parts: inference of the marginals and inference of the dependence. This is particularly important in the area of credit risk where information on dependence is ...

In this research we introduce a new class of multivariate probability models to the marketing literature. Known as “copula models”, they have a number of attractive features. First, they permit the combination of any univariate marginal distributions that need not come from the same distributional family. Second, a particular class of copula models, called “elliptical copula”, have the property...

The entropy theory has been widely applied in hydrology for probability inference based on incomplete information and the principle of maximum entropy. Meanwhile, copulas have been extensively used for multivariate analysis and modeling the dependence structure between hydrologic and climatic variables. The underlying assumption of the principle of maximum entropy is that the entropy variables ...

Although the copula literature has many instances of bivariate copulas, once more than two variates are correl ated, the choice of copulas often comes down to selection of the degrees-of-freedom parameter in the t-copula. In search for a wider selection of multivariate copulas we review a generalization of the t-copula and some copulas defined by Harry Joe. Generalizing the t-copula gives more ...

Copula modelling has in the past decade become a standard tool in many areas of applied statistics. However, a largely neglected aspect concerns the design of related experiments. Particularly the issue of whether the estimation of copula parameters can be enhanced by optimizing experimental conditions and how robust all the parameter estimates for the model are with respect to the type of copu...

As multivariate distributions with uniform one-dimensional margins, copulas provide very convenient models for studying dependence structure with tools that are scale-free. Each copula (n-copula) represents the whole class of continuous bivariate (multivariate) distributions from which it has been obtained when one-dimensional marginals were transformed by their cdf’s. The similar property, how...

We propose the ‘Copula PC’ algorithm for causal discovery from a combination of continuous and discrete data, assumed to be drawn from a Gaussian copula model. It is based on a two-step approach. The first step applies Gibbs sampling on rank-based data to obtain samples of correlation matrices. These are then translated into an average correlation matrix and an effective number of data points, ...