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1 LPMA - Laboratoire de Probabilités et Modèles Aléatoires

Abstract : We propose a generalized version of the Dantzig selector. We show that it satisfies sparsity oracle inequalities in prediction and estimation. We consider then the particular case of high-dimensional linear regression model selection with the Huber loss function. In this case we derive the sup-norm convergence rate and the sign concentration property of the Dantzig estimators under a mutual coherence assumption on the dictionary.

Keywords : Dantzig Sparsity Prediction Estimation Sign consistency





Autor: Karim Lounici -

Fuente: https://hal.archives-ouvertes.fr/



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