Evidence map›Paper›PMID 40621410›Full record

ArticleJournal of the American Statistical Association2025

Inferring independent sets of Gaussian variables after thresholding correlations.

Arkajyoti Saha, Daniela Witten, Jacob Bien

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Statistical Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Arkajyoti SahaDepartment of Statistics, University of Washington.
Daniela WittenDepartment of Statistics, University of Washington.
Jacob BienDepartment of Data Sciences and Operations, University of Southern California.

Funding

A Modeling Framework for Multi-View Data, with Applications to the Pioneer 100 Study and Protein Interaction NetworksR01GM123993 · NIGMS · UNIVERSITY OF WASHINGTON · PI BIEN, JACOB, WITTEN, DANIELA · 2017 to 2020
$1.3M
Models and Methods for Calcium Imaging Data with Application to the Allen Brain ObservatoryR01EB026908 · NIBIB · UNIVERSITY OF WASHINGTON · PI BUICE, MICHAEL, WITTEN, DANIELA · 2018 to 2020
$1.1M
CRCNS: Theory and Experiments to Elucidate Neural Coding in the Reward CircuitR01DA047869 · NIDA · UNIVERSITY OF WASHINGTON · PI WITTEN, DANIELA, WITTEN, ILANA · 2018 to 2021
$1.0M
NIBIB NIH HHS R01 EB026908NIDA NIH HHS R01 DA047869NIGMS NIH HHS R01 GM123993
6 · The paper itself

Abstract

We consider testing whether a set of Gaussian variables, selected from the data, is independent of the remaining variables. This set is selected via a very simple approach: these are the variables for which the correlation with all other variables falls below some threshold. Unlike other settings in selective inference, failure to account for the selection step leads to excessively

Indexed as

Canonical correlation analysiscorrelation thresholdinggene co-expression networkselective inference

Identifiers

PMID40621410
PMCPMC12224182

What OpenQuestion holds

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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.