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• W1512251330 abstract "In the context of a linear model with a sparse coefficient vector, exponential weights methods have been shown to be achieve oracle inequalities for prediction. We show that such methods also succeed at variable selection and estimation under the necessary identifiability condition on the design matrix, instead of much stronger assumptions required by other methods such as the Lasso or the Dantzig Selector. The same analysis yields consistency results for Bayesian methods and BIC-type variable selection under similar conditions." @default.
• W1512251330 created "2016-06-24" @default.
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• W1512251330 date "2012-08-13" @default.
• W1512251330 modified "2023-09-27" @default.
• W1512251330 title "Variable Selection with Exponential Weights and $l_0$-Penalization" @default.
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• W1512251330 hasPublicationYear "2012" @default.
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