A similarity and a difference between bivariate negative binomial distribution geometric is presented. The of the corresponding characteristic function are

Dependence Measures in Bivariate Gamma Frailty Models Bivariate duration data frequently arise in economics, biostatistics and other areas. In “bivariate frailty models”, dependence between the frailties (i.e., unobserved determinants) induces dependence between the durations. Using notions of quadrant dependence, we study restrictions that this imposes on the implied dependence of the duration...

A class of symmetric bivariate uniform distributions is proposed for use in statistical modeling. The distributions may be constructed to be absolutely continuous with correlations as close to ±1 as desired. Expressions for the correlations, regressions and copulas are found. An extension to three dimensions is proposed.

We point out that some of the results in Kundu and Gupta [3] in otherwise an excellent paper are incorrect. We propose a more general class of distributions and illustrate its use with two real data sets.

In the area of stress-strength models, there has been a large amount of work as regards estimation of the reliability R = Pr(X < Y). The algebraic form for R = Pr(X < Y) has been worked out for the vast majority of the well-known distributions when X and Y are independent random variables belonging to the same univariate family. In this paper, we consider forms of R when (X ,Y) follows a bivari...

A new class of multivariate discrete distributions with binomial and multinomial marginals is studied. This class of distributions is obtained in a natural manner using probabilistic properties of the sampling model considered. Some possible applications in game theory, life testing and exceedance models for order statistics are discussed.

A new class of bivariate distributions is introduced and studied, which encompasses Archimedean copulas and extreme value distributions as special cases. Its dependence structure is described, its maximum and minimum attractors are determined, and an algorithm is given for generating observations from any member of this class. It is also shown how it is possible to construct distributions in th...

The ordinary notion of a bivariate distribution has a natural generalisation. For this generalisation it is shown that a bivariate distribution can be characterised by a Hilbert space .%’ and a family da , 0 < p < 1, of subspaces of Z’. X specifies the marginal distributions whilst-X, is a summary of the dependence structure. This characterisation extends existing ideas on canonical correlation.