to analyze and manage the risk of drought in a region, access to some certain information is required, the most important of which is the frequency of drought severity-duration. realizing the high correlation of these factors, one must pick up a method that pinpoint the relation and effects of these factors on drought analysis. in this paper, numerous copula families were employed to model the ...

Copulas are used to depict dependence among several random variables. Both parametric and non-parametric estimation methods have been studied in the literature. Moreover, profile empirical likelihood methods based on either empirical copula estimation or smoothed copula estimation have been proposed to construct confidence intervals of a copula. In this paper, a jackknife empirical likelihood m...

The theory of copulas is known to provide a useful tool for modelling dependence in integrated risk management. In this paper, we describe how may be used copula methodology for the Monte Carlo Analysis whereas the main emphasis is put on Value-at-Risk as a risk measure. In the second part of this paper we show properties more generalised model as measurable space and we show how it is possible...

Formulae for the distribution of the losses of a loan portfolio that are both realistic and simple enough to be implemented in a spreadsheet are hard to come by. The most prominent example is the Vasicek (1987) formula which is based upon a simplified version of the multivariate Merton (1974) model. Using an algorithm from the theory of Archimedean Copula functions, this paper gives some more l...

The CUB model is a mixture distribution recently proposed in literature for modelling ordinal data. In this article we investigate how this univariate distribution can be exploited in order to model the margins of multivariate ordinal data. In particular, we take the theory of discrete copula into consideration and we show how use the Plackett distribution in order to construct a one parameter ...

We propose a comprehensive Bayesian approach for graphical model determination in observational studies that can accommodate binary, ordinal or continuous variables simultaneously. Our new models are called copula Gaussian graphical models (CGGMs) and embed graphical model selection inside a semiparametric Gaussian copula. The domain of applicability of our methods is very broad and encompass m...

Copulas were introduced by Sklar [13] to capture the stochastic dependence structure of random variables. Recall that for n ≥ 2, a function C : [0, 1] → [0, 1] is called an n-dimensional copula (n-copula, for short) whenever it is a restriction of an ndimensional distribution function with all univariate margins uniformly distributed on [0, 1]. Hence an n-copula is characterized by the properties:

We propose a comprehensive Bayesian approach for graphical model determination in observational studies that can accommodate binary, ordinal or continuous variables simultaneously. Our new models are called copula Gaussian graphical models (CGGMs) and embed graphical model selection inside a semiparametric Gaussian copula. The domain of applicability of our methods is very broad and encompasses...