Wasserstein F-tests for Fréchet regression on Bures-Wasserstein manifolds.

Haoshu Xu, Hongzhe Li

Abstract read

In one paragraph

Article in Journal of machine learning research : JMLR, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

2 authors.

Haoshu XuGraduate Group in Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA.

Hongzhe LiDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA.

Funding

Methods for Integrative Genomic Data AnalysisR01GM129781 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI Hongzhe Lee · 2018 to 2026

$3.5M

Penn-DISAM: Data Integration and Statistical Analysis Methods for dGTEx DataU01HG013841 · NHGRI · UNIVERSITY OF PENNSYLVANIA · PI GAO, ZIYUE, LEE, HONGZHE · 2024 to 2024

$1.9M

NHGRI NIH HHS U01 HG013841NIGMS NIH HHS R01 GM129781

6 · The paper itself

Abstract

This paper addresses regression analysis for covariance matrix-valued outcomes with Euclidean covariates, motivated by applications in single-cell genomics and neuroscience where covariance matrices are observed across many samples. Our analysis leverages Fréchet regression on the Bures-Wasserstein manifold to estimate the conditional Fréchet mean given covariates

Indexed as

Fréchet regressionFunctional calculusHypothesis testingOptimal transportWasserstein distance

Identifiers

PMID42841064

PMCPMC13641123

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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.