In this paper, we study the concomitants of dual generalized order statistics (and consequently generalized order statistics) when the parameters ${\gamma }_1,\dots ,{\gamma }_n$ are assumed to be pairwise different from Huang–Kotz Farlie–Gumble–Morgenstern bivariate distribution. Some useful recurrence relations between single and product moments of concomitants are obtained. Moreover, Shannon’s entropy and the Fisher information number measures are derived. Finally, these measures are extensively studied for some well-known distributions such as exponential, Pareto and power distributions. The main motivation of the study of the concomitants of generalized order statistics (as an important practical kind to order the bivariate data) under this general framework is to enable researchers in different fields of statistics to use some of the important models contained in these generalized order statistics only under this general framework. These extended models are frequently used in the reliability theory, such as the progressive type-II censored order statistics.

In this article, we study the concomitants of m-generalized order statistics (m-GOS) from iterated Farlie–Gumbel–Morgenstern bivariate distributions, denoted by IFGM $(\lambda ,\eta ),$ as an extension of several recent papers. Furthermore, the joint distribution of m-GOS of concomitants for this family are studied. Some useful recurrence relations between moments of concomitants are obtained. The generalized exponential distribution is taken a typical case of the possible marginals of the IFGM $(\lambda ,\eta ).$ Finally, the asymptotic behavior of the concomitant’s rank for the model ordinary order statistics is studied.

In this paper, we study the concomitants of generalized order statistics from iterated Farlie-Gumbel-Morgenstern (FGM) bivariate distribution. Three information measures, the Shannon entrop...