Adaptive Covariance Estimation with model selection
  We provide in this paper a fully adaptive penalized procedure to select a covariance among a collection of models observing i.i.d replications of the process at fixed observation points. For this we generalize previous results of Bigot and al. and propose to use a data driven penalty to obtain an oracle inequality for the estimator. We prove that this method is an extension to the matricial regression model of the work by Baraud. 
Rolando Biscay (IMFAV-DEUV), H\'el\`ene Lescornel (IMT), Jean-Michel   Loubes (IMT)
math.ST stat.TH