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dc.contributor.author
Zhou, Shuheng
dc.contributor.author
van de Geer, Sara
dc.contributor.author
Bühlmann, Peter
dc.date.accessioned
2017-06-11T05:32:23Z
dc.date.available
2017-06-11T05:32:23Z
dc.date.issued
2009
dc.identifier.uri
http://hdl.handle.net/20.500.11850/81080
dc.description.abstract
We 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.language.iso
en
dc.publisher
Cornell University
dc.title
Adaptive Lasso for High Dimensional Regression and Gaussian Graphical Modeling
dc.type
Working Paper
ethz.pages.start
arXiv:0903.2515
ethz.size
30 p.
ethz.notes
.
ethz.publication.place
Ithaca, NY
ethz.publication.status
published
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02000 - Dep. Mathematik / Dep. of Mathematics::02537 - Seminar für Statistik (SfS) / Seminar for Statistics (SfS)::03717 - van de Geer, Sara / van de Geer, Sara
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02000 - Dep. Mathematik / Dep. of Mathematics::02537 - Seminar für Statistik (SfS) / Seminar for Statistics (SfS)::03717 - van de Geer, Sara / van de Geer, Sara
ethz.identifier.url
http://arxiv.org/abs/0903.2515
ethz.date.deposited
2017-06-11T05:33:26Z
ethz.source
ECIT
ethz.identifier.importid
imp593651ab9684b23264
ethz.ecitpid
pub:127568
ethz.eth
yes
ethz.availability
Metadata only
ethz.rosetta.installDate
2017-07-17T08:39:31Z
ethz.rosetta.lastUpdated
2018-11-02T13:36:56Z
ethz.rosetta.versionExported
true
ethz.COinS
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