Hard thresholding regression.
Lasso
best subset selection
linear programming
oracle property
sparsity
variable selection
Journal
Scandinavian journal of statistics, theory and applications
ISSN: 0303-6898
Titre abrégé: Scand Stat Theory Appl
Pays: England
ID NLM: 0427163
Informations de publication
Date de publication:
Mar 2019
Mar 2019
Historique:
entrez:
19
10
2020
pubmed:
1
3
2019
medline:
1
3
2019
Statut:
ppublish
Résumé
In this paper, we propose the hard thresholding regression (HTR) for estimating high-dimensional sparse linear regression models. HTR uses a two-stage convex algorithm to approximate the
Identifiants
pubmed: 33071430
doi: 10.1111/sjos.12353
pmc: PMC7558802
mid: NIHMS1633915
doi:
Types de publication
Journal Article
Langues
eng
Pagination
314-328Subventions
Organisme : NIGMS NIH HHS
ID : R01 GM070335
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH086633
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH116527
Pays : United States
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