Evidence map›Paper›PMID 37799490›Full record

ArticleStatistica Sinica2023

Use of random integration to test equality of high dimensional covariance matrices.

Yunlu Jiang, Canhong Wen, Yukang Jiang, Xueqin Wang, Heping Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Statistica Sinica, 2023. 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. 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

5 authors.

Yunlu JiangJinan University, University of Science and Technology of China, Sun Yat-Sen University, Yale University.
Canhong WenJinan University, University of Science and Technology of China, Sun Yat-Sen University, Yale University.
Yukang JiangJinan University, University of Science and Technology of China, Sun Yat-Sen University, Yale University.
Xueqin WangJinan University, University of Science and Technology of China, Sun Yat-Sen University, Yale University.
Heping ZhangJinan University, University of Science and Technology of China, Sun Yat-Sen University, Yale University.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Analysis of Big Data Squared in Biomedical StudiesR01MH116527 · NIMH · YALE UNIVERSITY · PI ZHANG, HEPING · 2018 to 2022
$2.3M
Analysis of Genomic and Complex DataR01HG010171 · NHGRI · YALE UNIVERSITY · PI ZHANG, HEPING · 2019 to 2022
$1.4M
NCATS NIH HHS UL1 TR001863NHGRI NIH HHS R01 HG010171NIMH NIH HHS R01 MH116527
6 · The paper itself

Abstract

Testing the equality of two covariance matrices is a fundamental problem in statistics, and especially challenging when the data are high-dimensional. Through a novel use of random integration, we can test the equality of high-dimensional covariance matrices without assuming parametric distributions for the two underlying populations, even if the dimension is much larger than the sample size. The asymptotic properties of our test for arbitrary number of covariates and sample size are studied in depth under a general multivariate model. The finite-sample performance of our test is evaluated through numerical studies. The empirical results demonstrate that our test is highly competitive with existing tests in a wide range of settings. In particular, our proposed test is distinctly powerful under different settings when there exist a few large or many small diagonal disturbances between the two covariance matrices.

Indexed as

Covariance matrixHigh-dimensional dataRandom integration

Identifiers

PMID37799490
PMCPMC10550010

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Registered trials

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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.