Test de validation de graphe.
vendredi 22 février 2008, 9h30 - 10h45
Gaussian graphical models are promising tools for analysing genetic networks. In many applications, the biologists have a previous knowledge of the genetic network and may want to assess the quality of their model thanks to gene expression data. That is why we are interested in the problem of testing the graph of a Gaussian graphical model. More precisely, we construct a procedure for testing the neighborhoods of a Gaussian graphical model. Our approach is based on the connection between local Markov property and conditional regression of a Gaussian random variable. Our testing procedure is feasible in a high dimensional setting. We control exactly the level of the test and we are able to exhibit non asymptotic results on the power of the test.