Concomitants of Record Values From a General Farlie-Gumbel-Morgenstern Distribution

In this paper, we discuss the distributions of concomitants of record values arising from a polynomial-type single parameter extension of general Farlie-Gumbel-Morgenstern bivariate distribution. We derive the single and the product moments of concomitants of record values generally for any marginal distributions. The results obtained are applied to two-parameters exponential marginal distributions. In this case, we show that the maximal positive correlation between the two variables is approximately =423. Best linear unbiased estimators based on concomitants of record values of some parameters involved in the distribution are derived. Moreover, we obtain predictors of concomitants of record values by two methods. Finally a numerical illustration is presented to highlight the theoretical results obtained

Residual and Past Entropies of Concomitants from Lai And Xie Extensions of Case-II of Generalized Order Statistics and its Dual

In this article, we consider a new extensions of Morgenstern family is Lai and Xie extensions and discuss their concomitants for case-II of generalized order statistics and case-II of dual generalized order statistics. Additionally, recurrence relation between moments is found for the recommended models. We have also derived the expression for the joint distribution of concomitants for case-II of generalized order statistics and its dual. The residual and past entropies are shown last

Using rotations to build non symmetric extensions of Amblard-Girard copulas

A copula is a function that completely describes the dependence structure between the marginal distributions. One of the most important para-metric family of copulas is the Farlie-Gumbel-Morgenstern (FGM) family. In practical applications this copula has been shown to be somewhat limited and a symmetric extension of this family, known as the Amblard-Girard copula, has been introduced. Basing on rotations, we propose a new non symmetric extension of this family

An extension of FGM distributions based on an univariate function

A copula is a function that completely describes the dependence structure between the marginal distributions. One of the most important para-metric family of copulas is the Farlie-Gumbel-Morgenstern (FGM) family. In practical applications this copula has been shown to be somewhat limited. We propose a new extension of this family based on the introduction of an univariate function

Survival Amblard-Girard copulas

A copula is a function that completely describes the dependence structure between the marginal distributions. One of the most important para-metric family of copulas is the Farlie-Gumbel-Morgenstern (FGM) family. In practical applications this copula has been shown to be somewhat limited and a symmetric extension of this family, known as the Amblard-Girard copula, has been introduced. The goal of this note is to prove that the survival copula associated with an Amblard-Girard copula still is an Amblard-Girard copula

(R2070) Poisson-Exponentiated Weibull Distribution: Properties, Applications and Extension

In this article, we introduce a new member of the Poisson-X family namely, the Poisson-exponentiated Weibull distribution. The statistical as well as the distributional properties of the new distribution are studied, and the performance of the maximum likelihood method of estimation is verified by a simulation study. The flexibility of the distribution is illustrated by a real data set. We develop and study a reliability test plan for the acceptance or rejection of a lot of products submitted for inspection when their lifetimes follow the new distribution. A real data example is also given to illustrate the feasibility of the sampling plan developed. Later, we introduce a bivariate analogue of the Poisson-exponentiated Weibull distribution called the Farlie-Gumbel-Morgenstern bivariate Poisson-exponentiated Weibull distribution and consider the concomitants of order statistics that arise from this bivariate distribution. The distribution theory of the concomitants of order statistics is also developed

Concomitants of Generalized Order Statistics from Farlie-Gumbel-Morgenstern Distributions

Generalized order statistics constitute a unifed model for ordered random variables that includes order statistics and record values among others. Here, we consider concomitants of generalized order statistics for the Farlie-Gumbel-Morgenstern bivariate distributions and study recurrence relations between their moments. We derive the joint distribution of concomitants of two generalized or- der statistics and obtain their product moments. Application of these results is seen in establishing some well known results given separately for order statistics and record values and obtaining some new results

On generalized sarmanov bivariate distributions

A class of bivariate distributions which generalizes the Sarmanov class is introduced. This class possesses a simple analytical form and desirable dependence properties. The admissible range for association parameter for given bivariate distributions are derived and the range for correlation coefficients are also presented.Publisher's Versio