Description: In the copula literature there are many bivariate distribution families but very few higher dimensional ones. Moreover, most of these are difficult to work with. Some of the bivariate families can be extended to more dimensions but in general the construction of distribution functions with more than two variables is a difficult problem. This paper introduces a construction method that is straightforward to implement and can produce multivariate distribution functions of any dimension.

Description: During the last decade, a vast activity had been observed in generalizing of the classical discrete distributions. The main idea was to apply the extended versions fur modeling different kinds of dependent count or frequency data structure in various fields. In the present paper we suggest extensions of the classical univariate geometric, negative binomial, Poisson, Bernoulli, binomial and logarithmic series distributions, by including an additional parameter. It has a natural interpretation in terms of zero-inflation, and because of this we named the corresponding generalized versions adding inflated-parameter.