In order to evaluate the importance of higher-order correlations in neural spike count codes, flexible statistical models of dependent multivariate spike counts are required. Copula families, parametric multivariate distributions that represent dependencies, can be applied to construct such models. We introduce the Frank mixture family as a new copula family that has separate parameters for all...

In this paper, we consider the mean residual lifetime of two-component parallel systems in the case of possibly dependent components. We provide some results and examine the behavior of the mean residual life function at the system level for the bivariate Farlie-GumbelMorgenstern (FGM) distribution. Applications of these results to evaluate the relationship between the mean residual life and th...

We consider a discrete-time insurance risk model, in which the financial risks constitute a stationary process with finite dimensional distributions of Farlie–Gumbel–Morgenstern type. We obtain an exact asymptotic formula for the ruin probability, reflecting the impact of this kind of association structure among the financial risks. r 2007 Elsevier B.V. All rights reserved.

A new family of distributions called the Kumaraswamy Rayleigh is defied and studied. Some its relevant statistical properties are derived. Many bivariate type G families using Farlie-Gumbel-Morgenstern, modified Farlie-Gumbel-Morgenstern copula, Clayton copula Renyi’s entropy The method maximum likelihood estimation used. special models based on log-logistic, exponential, Weibull, Rayleigh, Par...

A copula is a useful tool for constructing bivariate and/or multivariate distributions. In this article, we consider a new modified class of FGM (Farlie–Gumbel–Morgenstern) bivariate copula for constructing several different bivariate Kumaraswamy type copulas and discuss their structural properties, including dependence structures. It is established that construction of bivariate distributions ...

In this paper we study the asymptotic behavior of the tail probabilities of sums of dependent and real-valued random variables whose distributions are assumed to be subexponential and not necessarily of dominated variation. We propose two general dependence assumptions under which the asymptotic behavior of the tail probabilities of the sums is the same as that in the independent case. In parti...

Correspondence analysis is a multivariate technique used to visualize categorical data, usually data in a two-way contingency table. Some extensions of correspondence analysis to a continuous bivariate distribution are presented, rstly from a canonical correlation analysis perspective and then from a continuous scaling perspective. These extensions are applied to the Farlie-Gumbel-Morgenstern (...

Recently, Rodríguez-Lallena and´Ubeda-Flores [8] have introduced a class of bivariate copulas which generalizes some known families such as the Farlie-Gumbel-Morgenstern distributions. In this paper we present multivariate generalizations of this class studying symmetry and dependence concepts, measuring the dependence among the components of each class, and providing several examples.

In this paper, the concomitants of case-II of generalized order statistics from Farlie-Gumbel-Morgenstern (FGM) distributions and recurrence relations between their moments are studied. Further, we provide the minimum variance linear unbiased estimator of the location and scale parameters of the concomitants of ordinary order statistics from some distributions.

We consider a generalization of the bivariate Farlie-Gumbel-Morgenstern (FGM) distribution by introducing additional parameters. For the generalized FGM distribution, the admissible range of the association parameter allowing positive quadrant dependence property is shown. Distributional properties of concomitants for this generalized FGM distribution are studied. Recurrence relations between m...