Evidence map›Paper›PMID 42305787›Full record

ArticleJournal of the American Statistical Association2026

Dimension Reduction for Large-Scale Federated Data: Statistical Rate and Asymptotic Inference.

Shuting Shen, Junwei Lu, Xihong Lin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Statistical Association, 2026. 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.

Shuting ShenNational University of Singapore.
Junwei LuDepartment of Biostatistics at Harvard T.H. Chan School of Public Health.
Xihong LinProfessor of Biostatistics at Harvard T.H. Chan School of Public Health and Professor of Statistics at Harvard University.

Funding

Translational Research Support CoreP30ES000002 · NIEHS · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI SUSAN A KORRICK · 1985 to 2026
$44.6M
Translating Molecular and Clinical Data to Population Lung Cancer Risk AssessmentU19CA203654 · NCI · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI Christopher I. Amos, Rayjean J. Hung · 2017 to 2026
$23.7M
Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer ResearchR35CA197449 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI XIHONG LIN · 2015 to 2026
$10.9M
Powering whole genome sequence-based genetic discovery for common human diseases- Extended 2021-2022.U01HG009088 · NHGRI · HARVARD SCHOOL OF PUBLIC HEALTH · PI LIN, XIHONG, NEALE, BENJAMIN MICHAEL · 2016 to 2021
$5.1M
Predictive Modeling of the Functional and Phenotypic Impacts of Genetic VariantsU01HG012064 · NHGRI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI Manuel Garber, XIHONG LIN · 2021 to 2026
$4.0M
Statistical Methods for Integrative Analysis of Large-Scale Whole Genome Sequencing Studies and Biobanks of Common DiseasesR01HL163560 · NHLBI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI XIHONG LIN · 2022 to 2026
$2.6M
NCI NIH HHS R35 CA197449NCI NIH HHS U19 CA203654NHGRI NIH HHS U01 HG009088NHGRI NIH HHS U01 HG012064NHLBI NIH HHS R01 HL163560NIEHS NIH HHS P30 ES000002
6 · The paper itself

Abstract

In light of the rapidly growing large-scale data in federated ecosystems, the traditional principal component analysis (PCA) is often not applicable due to privacy protection considerations and large computational burden. Algorithms were proposed to lower the computational cost, but few can handle both high dimensionality and massive sample size under distributed settings. In this paper, we propose the FAst DIstributed (FADI) PCA method for federated data when both the dimension

Indexed as

Computational efficiencyDistributed computingFast PCALarge-scale inferenceRandom sketches

Identifiers

PMID42305787
PMCPMC13268398

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.