Evidence map›Paper›PMID 40857499›Full record

ArticleJournal of the American Statistical Association2025

A Novel Approach of High Dimensional Linear Hypothesis Testing Problem.

Zhe Zhang, Xiufan Yu, Runze Li

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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. A Novel Approach of High Dimensional Linear Hypothesis Testing Problem.Journal of the American Statistical Association · 2025
    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.

Zhe ZhangDepartment of Statistics, The Pennsylvania State University, USA.
Xiufan YuDepartment of Applied and Computational Mathematics and Statistics, University of Notre Dame, USA.
Runze LiDepartment of Statistics, The Pennsylvania State University, USA.

Funding

Leveraging Big Data Science to Focus the HIV Response in Countries with Generalized HIV EpidemicsR01AI170249 · NIAID · JOHNS HOPKINS UNIVERSITY · PI BARAL, STEFAN DAVID · 2022 to 2025
$2.9M
Collaborative Research: DMS/NIGMS 2: New statistical methods, theory, and software for microbiome dataR01GM152812 · NIGMS · PENNSYLVANIA STATE UNIVERSITY, THE · PI Lingzhou Xue, Xiufan Yu · 2023 to 2026
$1.2M
NIAID NIH HHS R01 AI170249NIGMS NIH HHS R01 GM152812
6 · The paper itself

Abstract

This paper proposes an innovative double power-enhanced testing procedure for inference on high-dimensional linear hypotheses in high-dimensional regression models. Through a projection approach that aims to separate useful inferential information from the nuisance one, our proposed test accurately accounts for the impact of high-dimensional nuisance parameters. We discover that with a carefully-designed projection matrix, the projection procedure enables us to transform the problem of interest into a test on moment conditions, from which we construct a

Indexed as

High-dimensional inferenceHigh-dimensional loading matrixHigh-dimensional nuisance parametersMoment conditionPower enhancement

Identifiers

PMID40857499
PMCPMC12323798

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.