We use a recent characterization of the d-dimensional Archimedean copulas as the survival copulas of d-dimensional simplex distributions (McNeil and Nešlehová (2009)) to construct new Archimedean copula families, and to examine the relationship between their dependence properties and the radial parts of the corresponding simplex distributions. In particular, a new formula for Kendall’s tau is d...

One of the features inherent in nested Archimedean copulas, also called hierarchical Archimedean copulas, is their rooted tree structure. In this paper, a nonparametric, rank-based method to estimate this structure is developed. Our approach consists in representing the rooted tree structure as a set of trivariate structures that can be estimated individually. Indeed, for any triple of variable...

In order to determine the risk capital for their aggregate portfolio, property and casualty insurance companies must fit a multivariate model to the loss triangle data relating to each of their lines of business. As an inadequate choice of dependence structure may have an undesirable effect on reserve estimation, a two-stage inference strategy is proposed in this paper to assist with model sele...

The extremal dependence of a random vector describes the tail behaviors of joint probabilities of the random vector with respect to that of its margins, and has been often studied by using the tail dependence function of its copula. A tail density approach is introduced in this paper to analyze extremal dependence of the copulas that are specified only by densities. The relation between the cop...

in the literature of life-testing, general progressive censoring has been studied extensively. but, all the results have been developed under the key assumption that the units undertest are independently distributed. in this paper, we study general progressively type-ii censored order statistics arising from identical units under test which are jointly distributed according to an archimedean co...