Adaptive Lasso for High Dimensional Regression and Gaussian Graphical Modeling

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dc.creatorZhou, Shuheng
dc.creatorvan de Geer, Sara
dc.creatorBühlmann, Peter
dc.date2009-03-13
dc.date.accessioned2026-07-07T12:52:40Z
dc.date.available2026-07-07T12:52:40Z
dc.descriptionWe show that the two-stage adaptive Lasso procedure (Zou, 2006) is consistent for high-dimensional model selection in linear and Gaussian graphical models. Our conditions for consistency cover more general situations than those accomplished in previous work: we prove that restricted eigenvalue conditions (Bickel et al., 2008) are also sufficient for sparse structure estimation.
dc.description30 pages
dc.identifierhttps://arxiv.org/abs/0903.2515
dc.identifierhttp://arxiv.org/abs/0903.2515
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/223373
dc.subjectStatistics Theory
dc.subjectMachine Learning
dc.titleAdaptive Lasso for High Dimensional Regression and Gaussian Graphical Modeling
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