3.1 Estimator selection with unknown variance (Christophe Giraud)

Update: 2014-12-04

Description

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.

Comments

In Channel

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3.1 Estimator selection with unknown variance (Christophe Giraud)

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3.1 Estimator selection with unknown variance (Christophe Giraud)

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3.1 Estimator selection with unknown variance (Christophe Giraud)