Some theoretical results on the Grouped Variables Lasso |
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Authors: | Ch Chesneau M Hebiri |
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Institution: | (1) Université de Caen, Caen, France;(2) Université Paris VII, Paris, France |
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Abstract: | We consider the linear regression model with Gaussian error. We estimate the unknown parameters by a procedure inspired by
the Group Lasso estimator introduced in 22]. We show that this estimator satisfies a sparsity inequality, i.e., a bound in
terms of the number of non-zero components of the oracle regression vector. We prove that this bound is better, in some cases,
than the one achieved by the Lasso and the Dantzig selector.
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Keywords: | Lasso Group Lasso variable selection sparsity penalized least squares |
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