On Sampling from the Multivariate t Distribution

On Sampling from the Multivariate t Distribution

Abstract The multivariate normal and the multivariate t distributions belong to the most widely used multivariate distributions in statistics, quantitative risk management, and insurance. In contrast to the multivariate normal distribution, the parameterization of the multivariate t distribution does not correspond to its moments. This, paired with a non-standard implementation in the R package...

On Sampling from Multivariate Distributions

Let X1, X2, . . . , Xn be a set of random variables. Suppose that in addition to the prior distributions of these random variables we are also given linear constraints relating them. We ask for necessary and sufficient conditions under which we can efficiently sample the constrained distributions, find constrained marginal distributions for each of the random variables, etc. We give a tight cha...

On the Multivariate T-Distribution and Some of its Applications

Estimation Methods for the Multivariate t Distribution

The known estimation and simulation methods for multivariate t distributions are reviewed. A review of selected applications is also provided. We believe that this review will serve as an important reference and encourage further research activities in the area.

Sampling from Linear Multivariate Densities

It is well known that the generation of random vectors with nonindependent components is difficult. Nevertheless, we propose a new and very simple generation algorithm for multivariate linear densities over point-symmetric domains. Among other applications it can be used to design a simple decompositionrejection algorithm for multivariate concave distributions.