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 ...

We propose a new framework for dependence structure learning via copula. Copula is a statistical theory on dependence and measurement of association. Graphical models are considered as a type of special case of copula families, named product copula. In this paper, a nonparametric algorithm for copula estimation is presented. Then a Chow-Liu like method based on dependence measure via copula is ...

The multivariate distribution of five main indices of Tehran stock exchange is approximated using a pair-copula model. A vine graphical model is used to produce an í µí±›-dimensional copula. This is accomplished using a flexible copula called a minimum information (MI) copula as a part of pair-copula construction. Obtained results show that the achieved model has a good level of approximation.

One standard way of considering a probability distribution on the unit ncube, [0, 1], due to Sklar (1959) [A. Sklar, Fonctions de répartition à n dimensions et leur marges, Publ. Inst. Statist. Univ. Paris 8 (1959) 229231], is to decompose it into its marginal distributions and a copula, i.e. a probability distribution on [0, 1] with uniform marginals. The definition of copula was extended by J...

A recent paper presents an extension of the skew-normal distribution which is a copula. Under this model, standardized marginal distributions are standard normal. The copula itself depends on familiar skewing construction based normal function. This concerned with two topics. First, number extensions Notably these include case in Student’s t, different degrees freedom allowed for each margin. I...

Parametric conditional copula models allow the copula parameters to vary with a set of covariates according to an unknown calibration function. In this paper we develop a flexible Bayesian method to estimate the calibration function of a bivariate conditional copula. We construct a prior distribution over the set of smooth calibration functions using a sparse Gaussian process (GP) prior for the...

Aggregation operators transform a finite number of inputs, called arguments, into a single output. They are applied in many theoretical and practical domains and in particular aggregation operators play important role in different approaches to decision making, where values to be aggregated are typically preference or satisfaction degrees. Many operators of different type have been considered i...

In recent years copulas turned out to be a promising tool in multivariate modelling, mostly with applications in actuarial sciences and hydrology. In short, copula is a function which allows modelling dependence structure between stochastic variables. The main advantage is that the copula approach can split the problem of constructing multivariate probability distributions into a part containin...

Multivariate hydrologic frequency analysis has been widely studied using: (1) commonly known joint distributions or copula functions with the assumption of univariate variables being independently identically distributed (I.I.D.) random variables; or (2) directly applying the entropy theory-based framework. However, for the I.I.D. univariate random variable assumption, the univariate variable m...

The generation of multivariate probability distributions follows several approaches. Within financial applications the emphasis has mostly been on two methodologies. The first is the elliptical methodology, where the leap from univariate to multivariate has taken place by constructing density functions that are functions of quadratic forms of the marginals. The second is the copula philosophy, ...