The multivariate survival distributions are used for description of dependent failure times (life spans) (Clayton and Cuzik 1985; Hougaard 1995, Yashin et al. 1995). The convenient representation of such distributions involves semi-parametric (copula) structure of multivariate survival function. Such structure is important in applications where the cause for dependence between life spans is the...

Improvement in reliability and production play a very important role in system design. The two key factors, considered in predicting system reliability, are failure distribution of the component and system configuration. This research discusses the mathematical modeling of a highly reliable complex system, which is in three states i.e. normal, partial failed (degraded state) and complete failed...

China is one of the regions with most frequent drought disasters and serious social economic losses. Agricultural natural disaster. Due to climate change, regional agricultural risk assessment has always been focus academic circle. This study takes Zunyi City as an example, which typical city karst landform development. The monthly precipitation data set ground meteorological observation statio...

‎One of the most useful tools for handling multivariate distributions of dependent variables in terms of their marginal distribution is a copula function‎. ‎The copula families capture a fair amount of attention due to their applicability and flexibility in describing the non-Gaussian spatial dependent data‎. ‎The particular properties of the spatial copula are rarely ...

In this paper, we consider different issues related to Archimedean copulae and positive dependence. In the first part, we characterize Archimedean copulae that possess positive dependence properties such as multivariate total positivity of order 2 ðMTP2Þ and conditionally increasingness in sequence. In the second part, we investigate conditions for exchangeable binary sequences to admit an Arch...

Convergence of a sequence of bivariate Archimedean copulas to another Archimedean copula or to the comonotone copula is shown to be equivalent with convergence of the corresponding sequence of Kendall distribution functions. No extra differentiability conditions on the generators are needed. r 2007 Elsevier B.V. All rights reserved.

The copula function is a multivariate distribution whose marginal distributions are uniformly distributed on the interval [0,1], this function called copula that ties the joint and the margins together. One important class of copula models is that of semiparametric copula models. In this paper, a semiparametric copula and its properties are introduced also a test of symmetry for semiparametric ...