probabilistic approach of traffic assignment has been primarily developed to provide a more realistic and flexible theoretical framework to represent traveler’s route choice behavior in a transportation network. the problem of path overlapping in network modelling has been one of the main issues to be tackled. due to its flexible covariance structure, probit model can adequately address the pro...

This paper focuses on a nonlinear stochastic model for financial simulation and forecasting based on assumptions of multivariate stochastic correlation, with an application to the European market. We present in particular the key elements of a structured hierarchical econometric model that can be used to forecast financial and commodity markets relying on statistical and simulation methods. The...

in some statistical process control applications, the quality of the product is characterized by thecombination of both correlated variable and attributes quality characteristics. in this paper, we propose anovel control scheme based on the combination of two multi-layer perceptron neural networks forsimultaneous monitoring of mean vector as well as the covariance matrix in multivariate-attribu...

Two important components of a large-scale stochastic simulation are the generation of random variates from multivariate input models and the analysis of simulation output data to estimate mean performance measures and confidence intervals. The common practice is to obtain the multivariate input models applying statistically valid fitting algorithms to historical data sets of finite length and c...

Calculation of Multivariate Normal Probabilities by Simulation, with Applications to Maximum Simulated Likelihood Estimation We discuss methods for calculating multivariate normal probabilities by simulation and two new Stata programs for this purpose: -mdrawsfor deriving draws from the standard uniform density using either Halton or pseudo-random sequences, and an egen function -mvnp()for calc...