According to Markowitz (1952) portfolio theory assumed that the investor has a concave utility function that expresses an attitude of risk aversion and managed to put portfolio selection based on two criteria, mean and variance. Other studies have improved this approach and following Basel II recommendations by using Value-at-Risk (VaR) as a standard risk measure in finance, Alexander & Baptist...

The question as to the role that correlated activity plays in the coding of information in the brain continues to be one of the most important in neuroscience. One approach to understanding this role is to formally model the ensemble responses as multivariate probability distributions. We have previously introduced alternatives to linear assumptions of multivariate Gaussian dependence for spike...

A new class of copulas, termed the MGL copula class, is introduced. The originates from extracting dependence function multivariate generalized log-Moyal-gamma distribution whose marginals follow univariate (GLMGA) as introduced in Li et al. (2021) . can capture nonelliptical, exchangeable, and asymmetric dependencies among marginal coordinates provides a simple formulation for regression appli...

The measure for expert dependence proposed by Jouini and Clemen (clemen) is implemented for expert judgement data gathered at the T.U. Delft. Experts show less dependence than might have been supposed, though more sensitive measures might reveal more. Clemen’s copula for aggregation is implemented and performance is compared with performance-based combinations for two illustrative cases.

BACKGROUND In survival studies when the event times are dependent, performing of the analysis by using of methods based on independent assumption, leads to biased. In this paper, using copula function and considering the dependence structure between the event times, a parametric joint distribution has made fitting to the events, and the effective factors on each of these events would be determi...

A copula density is the joint probability density function (PDF) of a random vector with uniform marginals. An approach to bivariate copula density estimation is introduced that is based on a maximum penalized likelihood estimation (MPLE) with a total variation (TV) penalty term. The marginal unity and symmetry constraints for copula density are enforced by linear equality constraints. The TV-M...

Graphical models are widely used to reason about high-dimensional domains. Yet, learning the structure of the model from data remains a formidable challenge, particularly in complex continuous domains. We present a highly accelerated structure learning approach for continuous densities based on the recently introduced Copula Bayesian Network representation. For two common copula families, we pr...

We study the relations between tail order of copulas and hidden regular variation (HRV) on subcones generated by order statistics. Multivariate regular variation (MRV) and HRV deal with extremal dependence of random vectors with Pareto-like univariate margins. Alternatively, if one uses copula to model the dependence structure of a random vector, then upper exponent and tail order functions can...