Selecting the number of components in PCA via random signflips
David Hong; Yue Sheng; Edgar Dobriban · 2026 · Journal of the American Statistical Association
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
Principal component analysis (PCA) is a foundational tool in modern data analysis, and a crucial step in PCA is selecting the number of components to keep. However, classical selection methods (e.g., scree plots, parallel analysis, etc.)…
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Metadata source: OpenAlex · DOI 10.1080/01621459.2026.2719861