Showing papers by "Jean-Michel Loubes published in 2006"
TL;DR: The authors use an SAEM‐type algorithm (a stochastic approximation of the EM algorithm) to select the densities of the mixture model and test the validity of their methodologies by forecasting short term travel times.
Abstract: The purpose of this work is, on the one hand, to study how to forecast road trafficking on high way networks and, on the other hand, to describe future traffic events. Here, road trafficking is measured by vehicle velocities. The authors propose two methodologies. The first is based on an empirical classification method, and the second on a probability mixture model. They use an SAEM-type algorithm (a stochastic approximation of the EM algorithm) to select the densities of the mixture model. Then, they test the validity of their methodologies by forecasting short term travel times.
19 citations
TL;DR: In this article, the consistency of a nonparametric maximum likelihood estimator for a class of stochastic inverse problems was established by embedding the framework into the general settings of early results of Pfanzagl related to mixtures.
13 citations