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Model-Consistent Sparse Estimation through the Bootstrap

Francis Bach 1
1 WILLOW - Models of visual object recognition and scene understanding
CNRS - Centre National de la Recherche Scientifique : UMR8548, Inria Paris-Rocquencourt, DI-ENS - Département d'informatique de l'École normale supérieure
Abstract : We consider the least-square linear regression problem with regularization by the $\ell^1$-norm, a problem usually referred to as the Lasso. In this paper, we first present a detailed asymptotic analysis of model consistency of the Lasso in low-dimensional settings. For various decays of the regularization parameter, we compute asymptotic equivalents of the probability of correct model selection. For a specific rate decay, we show that the Lasso selects all the variables that should enter the model with probability tending to one exponentially fast, while it selects all other variables with strictly positive probability. We show that this property implies that if we run the Lasso for several bootstrapped replications of a given sample, then intersecting the supports of the Lasso bootstrap estimates leads to consistent model selection. This novel variable selection procedure, referred to as the Bolasso, is extended to high-dimensional settings by a provably consistent two-step procedure.
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Submitted on : Tuesday, January 20, 2009 - 8:56:36 PM
Last modification on : Friday, October 15, 2021 - 1:40:04 PM
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  • HAL Id : hal-00354771, version 1
  • ARXIV : 0901.3202



Francis Bach. Model-Consistent Sparse Estimation through the Bootstrap. 2009. ⟨hal-00354771⟩



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