We propose a family of copula-based multivariate distributions with g-and-h marginals. After studying the properties of the distribution, we develop a two-step estimation strategy and analyze via ...

We start with analyzing stochastic dependence in a classic bivariate normal density framework. We focus on the way the conditional density of one of the random variables depends on realizations of the ...

We apply distortion functions to bivariate survival functions for nonnegative random variables. This leads to a natural extension of univariate distortion risk measures to the multivariate setting.

Probabilistic Deep Learning finds its application in autonomous vehicles and medical diagnoses. This is an increasingly important area of deep learning that aims to quantify the noise and uncertainty ...

Abstract: Univariate Mixed Poisson distributions (MPDs) are commonly used to model data recorded from low flux objects or with short exposure times. They assume that the number of recorded events, ...

Abstract: In this article, the tail dependence coefficient of the multivariate elliptical distribution and its properties are obtained. Furthermore, some specific tail dependence coefficients (TDC) ...