Reference health
Least Squares After Model Selection in High-Dimensional Sparse Models
25/25 checkable references clean · checked 2026-08-28
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
9 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 25 checked references that resolve
resolves10.1016/s0022-0000(03)00025-4
Database-friendly random projections: Johnson-Lindenstrauss with binary coins
resolves10.1214/074921708000000101
Consistent selection via the Lasso for high dimensional approximating regression models
resolves10.1214/009053606000001523
The Dantzig selector: Statistical estimation when p is much larger than n
resolves10.1002/cpa.20132
For most large underdetermined systems of linear equations the minimal 𝓁
<sub>1</sub>
‐norm solution is also the sparsest solution
resolves10.1109/tit.2005.860430
Stable recovery of sparse overcomplete representations in the presence of noise
resolves10.1111/j.1467-9868.2008.00674.x
Sure Independence Screening for Ultrahigh Dimensional Feature Space
resolves10.1016/j.aim.2006.03.013
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.gif" display="inline" overflow="scroll"><mml:msub><mml:mi>L</mml:mi><mml:mi>p</mml:mi></mml:msub></mml:math>-moments of random vectors via majorizing measures
resolves10.1214/08-ejs177
Sup-norm convergence rate and sign concentration property of Lasso and Dantzig estimators
resolves10.1109/tit.2009.2016018
Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso)
resolves10.1214/07-aos520
The sparsity and bias of the Lasso selection in high-dimensional linear regression
The 9 references without a DOI — listed, not checked
no DOI — not checkedSupplementary material: Supplement to "?1-penalized quantile regression in high-dimensional sparse models
no DOI — not checkedref13
no DOI — not checkedref14
no DOI — not checkedTaking advantage of sparsity in multi-task learning
no DOI — not checkedref21
no DOI — not checkedref27
no DOI — not checkedref29
no DOI — not checkedref32
no DOI — not checkedOn model selection consistency of Lasso
What this badge says. CiteStamped means the CHECKABLE references of this
work were clean at the dated check: each resolved to a known work in a public registry, and none
carried a retraction notice at that time. It says nothing about the quality, findings,
or importance of the work itself, and nothing about references deposited without a DOI.
checked 2026-08-28 — re-checked daily as this page is visited; titles and statuses come from Crossref and DataCite and are not part of the signed record
Embed this badge
Both snippets point at the live badge image and link back to this page. The badge re-renders from the daily check, so an embed never goes stale by more than a day of visits.
<a href="https://citestamp.com/citestamped/10.2139/ssrn.1582594"><img src="https://citestamp.com/citestamped/10.2139/ssrn.1582594/badge.svg" alt="CiteStamped reference-health badge" width="460" height="64"></a>
[](https://citestamp.com/citestamped/10.2139/ssrn.1582594)