Using a Supervised Principal Components Analysis for Variable Selection in High-Dimensional Datasets Reduces False Discovery Rates.

Ullah I, Mengersen K, Pettitt AN, Liquet B

DOI: 10.1002/sim.70110
Published: 2025 Jun
Container: Statistics in medicine
Publisher: Not recorded
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1002/sim.70110,
  title = {Using a Supervised Principal Components Analysis for Variable Selection in High-Dimensional Datasets Reduces False Discovery Rates.},
  author = {Ullah I and Mengersen K and Pettitt AN and Liquet B},
  year = {2025},
  journal = {Statistics in medicine},
  doi = {10.1002/sim.70110},
  url = {https://doi.org/10.1002/sim.70110}
}

RIS

TY  - JOUR
TI  - Using a Supervised Principal Components Analysis for Variable Selection in High-Dimensional Datasets Reduces False Discovery Rates.
AU  - Ullah I
AU  - Mengersen K
AU  - Pettitt AN
AU  - Liquet B
PY  - 2025
JO  - Statistics in medicine
DO  - 10.1002/sim.70110
UR  - https://doi.org/10.1002/sim.70110
ER  -

APA

I, U., K, M., AN, P., & B, L. (2025). Using a Supervised Principal Components Analysis for Variable Selection in High-Dimensional Datasets Reduces False Discovery Rates.. Statistics in medicine. https://doi.org/10.1002/sim.70110

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pubmed · retrieved 2026-09-25T16:25:49.881Z