Evidence map›Paper›PMID 40641907›Full record

ArticleJournal of the American Statistical Association2025

Model-Free Statistical Inference on High-Dimensional Data.

Xu Guo, Runze Li, Zhe Zhang, Changliang Zou

Abstract read
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

4 authors.

Xu GuoSchool of Statistics, Beijing Normal University, China.
Runze LiDepartment of Statistics, The Pennsylvania State University, USA.
Zhe ZhangDepartment of Statistics, The Pennsylvania State University, USA.
Changliang ZouSchool of Statistics and Data Science, Nankai University, China.

Funding

Statistical Models for Estimating and Projecting HIV/AIDS EpidemicsR01AI136664 · NIAID · PENNSYLVANIA STATE UNIVERSITY, THE · PI BAO, LE · 2017 to 2021
$3.6M
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
NIAID NIH HHS R01 AI136664NIAID NIH HHS R01 AI170249
6 · The paper itself

Abstract

This paper aims to develop an effective model-free inference procedure for high-dimensional data. We first reformulate the hypothesis testing problem via sufficient dimension reduction framework. With the aid of new reformulation, we propose a new test statistic and show that its asymptotic distribution is

Indexed as

False discovery rate controlMarginal coordinate hypothesisOrthogonalitySufficient dimension reduction

Identifiers

PMID40641907
PMCPMC12240534

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.