A general framework for testing clustering significance and variable-level inference in high-dimensional data

Hui Shen, Dongmei Li, Yufeng Liu

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DOI: 10.1093/biomtc/ujag145
Published: 2026-07-01
Container: Biometrics
Publisher: Oxford University Press (OUP)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1093/biomtc/ujag145,
  title = {A general framework for testing clustering significance and variable-level inference in high-dimensional data},
  author = {Hui Shen and Dongmei Li and Yufeng Liu},
  year = {2026},
  journal = {Biometrics},
  doi = {10.1093/biomtc/ujag145},
  url = {https://doi.org/10.1093/biomtc/ujag145}
}

RIS

TY  - JOUR
TI  - A general framework for testing clustering significance and variable-level inference in high-dimensional data
AU  - Hui Shen
AU  - Dongmei Li
AU  - Yufeng Liu
PY  - 2026
JO  - Biometrics
DO  - 10.1093/biomtc/ujag145
UR  - https://doi.org/10.1093/biomtc/ujag145
ER  -

APA

Shen, H., Li, D., & Liu, Y. (2026). A general framework for testing clustering significance and variable-level inference in high-dimensional data. Biometrics. https://doi.org/10.1093/biomtc/ujag145

Source records

crossref · retrieved 2026-09-25T06:08:48.767Z