Bivariate Distributions with Given Marginals

Bivariate Distributions with Given Extreme Value Attractor

A new class of bivariate distributions is introduced and studied, which encompasses Archimedean copulas and extreme value distributions as special cases. Its dependence structure is described, its maximum and minimum attractors are determined, and an algorithm is given for generating observations from any member of this class. It is also shown how it is possible to construct distributions in th...

Estimation of Linear Functionals of Bivariate Distributions with Parametric Marginals

Local Smoothing with given Marginals

In models using categorical data one may use adjacency relations to justify smoothing to improve upon simple histogram approximations of the probabilities. This is particularly convenient for sparsely observed or rather peaked distributions. Moreover, in a few models, prior knowledge of a marginal distribution is available. We adapt local polynomial estimators to include this partial informatio...

Non-linear Integral Equations to Approximate Bivariate Densities with given Marginals and Dependence Function

The local dependence function, introduced by Holland and Wang (1987) and studied by Wang (1993) as a continuous version of the local cross-ratio, describes the local relation between two random variables. Three explicit numerical algorithms are proposed to approximate bivariate densities given the marginal densities and the local dependence function. This approach is suited for simulation purpo...

Recent developments on the construction of bivariate distributions with fixed marginals

Constructing a bivariate distribution with specific marginals and correlation has been a challenging problem since 1930s. In this survey we shall focus on the recent developments on the FGM-related distributions, including Sarmanov and Lee’s distributions, Baker’s distributions and Bayramoglu’s distributions. This complements the most recent works of (i) the review by Sarabia and Gómez-Déniz (2...