[Statlist] Séminaire Institut de Statistique mardi 4/12/2007

[ISTAT Messagerie]

SEMINAIRE DE STATISTIQUE
Institut de Statistique,
Université de Neuchâtel Pierre à Mazel, 7 
(1er étage, salle 110), Neuchâtel 
http://www2.unine.ch/statistics
 
Mardi 4 décembre 2007, 11h00
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Peter Buehlmann, ETH Zurich

Title : Variable selection for high-dimensional data with applications in molecular biology 

In many application areas, the number of covariates is very large (e.g. in the thousands) while the sample size is quite small (e.g. in the dozens). Standard exhaustive search methods for variable selection quickly become computationally infeasible, and forward selection methods are typically very unstable. 
We will show that in generalized linear models, L1-penalty methods (Lasso) can be very powerful as a first step: with high probability, the (mathematical) true model is a subset of the estimated model. Moreover, some adaptations correct Lasso's overestimation behavior, yielding consistent variable selection schemes, and their exhaustive computation can be done very efficiently.  
Our illustrations cover both theory and methodology as well as concrete applications in molecular biology.