Evidence map›Paper›PMID 26985140›Full record

ArticleStatistica Sinica2015

A Small-Sample Choice of the Tuning Parameter in Ridge Regression.

Philip S Boonstra, Bhramar Mukherjee, Jeremy M G Taylor

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Statistica Sinica, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Philip S BoonstraDepartment of Biostatistics, University of Michigan, Ann Arbor 48109.
Bhramar MukherjeeDepartment of Biostatistics, University of Michigan, Ann Arbor 48109.
Jeremy M G TaylorDepartment of Biostatistics, University of Michigan, Ann Arbor 48109.

Funding

XenograftP30CA046592 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI THEODORE S LAWRENCE · 1988 to 2026
$178.2M
Biostatistics Training Program in Cancer ResearchT32CA083654 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Veerabhadran Baladandayuthapani, Kelley McLain Kidwell · 2002 to 2026
$4.3M
Statistical Methods for Cancer BiomarkersR01CA129102 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GHOSH, DEBASHIS, TAYLOR, JEREMY M.G. · 2009 to 2021
$3.3M
NCI NIH HHS P30 CA046592NCI NIH HHS R01 CA129102NCI NIH HHS T32 CA083654
6 · The paper itself

Abstract

We propose new approaches for choosing the shrinkage parameter in ridge regression, a penalized likelihood method for regularizing linear regression coefficients, when the number of observations is small relative to the number of parameters. Existing methods may lead to extreme choices of this parameter, which will either not shrink the coefficients enough or shrink them by too much. Within this "small-

Indexed as

Akaike’s information criterionCross-validationGeneralized cross-validationHyperpenaltyMarginal likelihoodPenalized likelihood

Identifiers

PMID26985140
PMCPMC4790465

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.