Sufficient dimension reduction via random‐partitions for the large‐p‐small‐n problem

Summary Sufficient dimension reduction (SDR) continues to be an active field of research. When estimating the central subspace (CS), inverse regression based SDR methods involve solving a generalized eigenvalue problem, which can be problematic under the large‐p‐small‐n situation. In recent years, n...

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Veröffentlicht in:	Biometrics 2019-03, Vol.75 (1), p.245-255
Hauptverfasser:	Hung, Hung, Huang, Su‐Yun
Format:	Artikel
Sprache:	eng
Schlagworte:	Alcoholism - pathology Algorithms Asymptotic methods Asymptotic properties Brain - drug effects Computer Simulation Distance correlation screening Eigenvalues Electroencephalography - statistics & numerical data Humans Linear algebra Machine Learning Models, Theoretical Partitions (mathematics) Random sketching Randomized algorithm Random‐partition Reduction Regression analysis Sketches Statistical analysis Statistical inference Subspaces Sufficient dimension reduction Sure screening property
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