On a bivariate Poisson-geometric distribution
In this paper, we study a general structure for the so-called Farlie-Gumbel-Morgenstern (FGM) family of bivariate distributions. Through examples we show how to use the proposed structure to study dependence properties of the FGM type distributions by a general approach.
We give a representation of the class of all -dimensional copulas such that, for a fixed , , all their -dimensional margins are equal to the independence copula. Such an investigation originated from an open problem posed by Schweizer and Sklar.
A four parameter trivariate Poisson distribution is considered. Recurrences for the probabilities and the partial derivatives of the probabilities with respect to the parameters are derived. Solutions of the maximum likelihood equations are obtaired and the determinant of their asymptotic covariance matrix is given. Applications of the maximum likelihood estimation technique to simulated data sets are also examined.
In this paper we derive conditions upon the nonnegative random variable under which the inequality holds for a fixed nonnegative constant and for any absolutely continuous function . Taking into account the characterization of a Gamma distribution we consider the functional and establishing some of its properties we show that and that iff the random variable has a Gamma distribution.
Assuming that is the copula function of and with marginal distribution functions and , in this work we study the selection distribution . We present some special cases of our proposed distribution, among them, skew-normal distribution as well as normal distribution. Some properties such as moments and moment generating function are investigated. Also, some numerical analysis is presented for illustration.