Using a supervised principal components analysis for variable selection in high-dimensional datasets reduces false discovery rates

Insha Ullah^*, Kerrie Mengersen, Anthony Pettitt, Benoit Liquet

^*Corresponding author for this work

Research output: Contribution to journal › Article

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@article{ed1a56e385474d0fa31f514cf702f20e,

title = "Using a supervised principal components analysis for variable selection in high-dimensional datasets reduces false discovery rates",

author = "Insha Ullah and Kerrie Mengersen and Anthony Pettitt and Benoit Liquet",

year = "2020",

month = may,

day = "15",

doi = "10.1101/2020.05.15.097774",

language = "English",

journal = "bioRxiv",

}