Copulas are used to specify dependence between two or more random variables. The last
few years have seen a surge of developments of parametric models for copulas. Here, we …

We introduce the successive iterations in the original FGM type bivariate-generalized
exponential distribution. Some distributional properties of concomitants of order statistics as …

The directional dependence between variables using asymmetric copula regression has
drawn much attention in recent years. There are, however, some critical issues which have …

This article proposes a new directional dependence by using the Gaussian copula beta
regression model. In particular, we consider an asymmetric Generalized AutoRegressive …

Featured Application This work can be applied to the research and development planning or
sustainable technology management. Abstract The technological keywords extracted from …

In order to accurately and flexibly capture the correlation structure between two random
coefficients in the binomial AR (1) process, we propose a new class of models with copula …

To examine the asymmetry of financial data in detail, we have considered both the tail
dependence with diverse copulas and Jung et al.'s [8] directional dependence by copula …

A copula is a function that completely describes the dependence structure between the
marginal distributions. One of the most important parametric family of copulas is the Farlie …

A family of conjugated distributions for a given type of copulas is defined in this paper. Those
copulas can be written as a mixture of d‐dimensional parameter exponential functions. The …

Multipopulation mortality modeling is a significant research problem in actuarial science.
Mortality functions involving multiple lives are also essential to determine the pricing of …