In this paper, we introduce a new kind of order, Cesaro supermodular order, which includes supermodular order and stochastic order. For this new order, we show that it almost fulfils all desirable properties of a multivariate positive dependence order that have been proposed by Joe (1997). Also, we obtain some relations between it with other orders. Finally, we consider different issues related...

Copulas recently emerged in many data analysis and knowledge discovery tasks as a flexible tool for modeling complex multivariate distributions. The paper presents a method for estimating copulas from one of the most popular classes of copulas, namely hierarchical Archimedean copulas. The method is based on the close relationship of the copula structure and the values of Kendall’s tau computed ...

This paper presents a general tail approximation method for evaluating the Valueat-Risk of any norm of random vectors with multivariate regularly varying distributions. The main result is derived using the relation between the intensity measure of multivariate regular variation and tail dependence function of the underlying copula, and in particular extends the tail approximation discussed in E...

We study the impact of certain transformations within the class of Archimedean copulas. We give some admissibility conditions for these transformations, and define some equivalence classes for both transformations and generators of Archimedean copulas. We extend the r-fold composition of the diagonal section of a copula, from r ∈ N to r ∈ R. This extension, coupled with results on equivalence c...

A new two-parameter lifetime distribution is proposed and numerically studied. The model has a flflexible failure rate shapes such as “monotonically increasing” , decreasing” “bathtub” “constant” “upside down” “J-shape” . Various of its statistical properties are derived. numerical analysis skewness kurtosis presented. Many bivariate multivariate extensions also presented via Farlie Gumbel Morg...

Existing works on multivariate distributions mainly focus on limited distribution functions and require that the associated marginal distributions belong to the same family. Although this simplifies problems, it may fail to deal with practical cases when the marginal distributions are arbitrary. To this end, copula function is employed since it provides a flexible way in decoupling the marginal...

In this paper, we address two important issues in semiparametric survival model selection for censored data generated by the Archimedean copula family: method of estimating the parametric copulas and data reuse. We demonstrate that for selection among candidate copula models that could all be misspecified, estimators of the parametric copulas based on minimizing the selection criterion function...

A copula is a means of generating an n-variate distribution function from an arbitrary set of n univariate distributions. For the class of portfolio allocators that are risk averse, we use the copula approach to identify a large set of n-variate asset return distributions such that the relative magnitudes of portfolio shares can be ordered according to the reversed hazard rate ordering of the n...

The problem of estimation multivariate survival function under dependent random right-censoring observations is considered. To construct estimators, Archimedean copula functions are used. Consistency properties estimators proved by martingale techniques. possibility application to integral-type functionals discussed.

Copulas are a general tool for assessing the dependence structure of random variables. Important properties as well as a number of examples are discussed, including Archimedean copulas and the Marshall-Olkin copula. As measures of the dependence we consider linear correlation, rank correlation, the coefficients of tail dependence and association. Copulas are a tool for modeling and capturing th...