3.1 Estimator selection with unknown variance

2 janvier 2017
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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 F = { ˆ f_ : _ 2 _}, an estimator hatfˆ_ 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.