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.

We provide in this paper simulation algorithms for one-sided and two-sided truncated normal distributions. These algorithms are then used to simulate multivariate normal variables with restricted parameter space for any covariance structure.

multivariate control charts such as hotelling`s t^ 2 and x^ 2 are commonly used for monitoring several related quality characteristics. these control charts use correlation structure that exists between quality characteristics in an attempt to improve monitoring. the purpose of this article is to discuss some issues related to the g chart proposed by levinson et al. [9] for detecting shifts in ...

For a cost-effective production of integrated circuits, one important aspect is the accurate simulation of electronic circuits with regard to process variation. Process variation is described as the range of simulation (SPICE) parameters, but to reduce the costs of simulation they are replaced by easy available e-test parameters. An approximate algorithm for the location depth (multivariate ran...

We try to understand the various components affecting the stability of tunneling and wellbore operations. We use a series of advanced finite element (FE) simulations to obtain a dataset which contains the stress response of the soil during drilling operations as a function of 12 input parameters. Using Multivariate Adaptive Regression Splines (MARS), we create a simplified model of the simulati...

Copulas have attracted considerable interest for modelling multivariate observations and for stress testing in quantitative finance. In this paper, a semiparametric method is studied for estimating the copula parameter and the joint distribution of the error term in a class of multivariate time series models when the marginal distributions of the errors are unknown. The proposed method first ob...

One of the most important aspects in asset allocation problems is the assumption concerning the probability distribution of asset returns. Financial managers generally suppose normal distribution, even if extreme realizations usually have an higher frequency than in the Gaussian case. We propose a Monte Carlo simulation approach in order to solve an asset allocation problem with shortfall const...

Mathematical models of highly interconnected and multivariate signalling networks provide useful tools to understand these complex systems. However, effective approaches to extracting multivariate regulation information from these models are still lacking. In this study, we propose a data-driven modelling framework to analyse large-scale multivariate datasets generated from mathematical models....