3.1 Estimator selection with unknown variance (Christophe Giraud)

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Par Universite Paris 1 Pantheon-Sorbonne, découvert par Player FM et notre communauté - Le copyright est détenu par l'éditeur, non par Player F, et l'audio est diffusé directement depuis ses serveurs. Appuyiez sur le bouton S'Abonner pour suivre les mises à jour sur Player FM, ou collez l'URL du flux dans d'autre applications de podcasts.
We consider the problem of Gaussian regression (possibly in a high- dimensional setting) when the noise variance is unknown. We propose a procedure which selects within any collection of estimators, an estimator hatf that nearly achieves the best bias/variance trade off. This selection procedure can be used as an alternative to Cross Validation to : - tune the parameters of a family of estimators - compare different families of estimation procedure - perform variable selection.

12 episodes