Sequential sufficient dimension reduction for large p, small n problems

Sequential sufficient dimension reduction for large p, small n problems

We propose a new and simple framework for dimension reduction in the large p, small n setting. The framework decomposes the data into pieces, thereby enabling existing approaches for n>p to be adapted to n

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Veröffentlicht in:Journal of the Royal Statistical Society. Series B, Statistical methodology Statistical methodology, 2015-09, Vol.77 (4), p.879-892
Hauptverfasser: Yin, Xiangrong, Hilafu, Haileab
Format: Artikel
Sprache:eng
Schlagworte:
Analysis
Asymptotic properties
Central subspace
covariance
Covariance matrices
Decomposition
Dimensionality
Dimensionality reduction
equations
Estimation methods
Evidence
Gene expression
High dimensional data
High dimensional spaces
Large p
Large p, small n
Leukemia
small n
Statistical variance
Statistics
Studies
Sufficient dimension reduction
Sufficient variable selection
Two dimensional modeling
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Beschreibung
Zusammenfassung:We propose a new and simple framework for dimension reduction in the large p, small n setting. The framework decomposes the data into pieces, thereby enabling existing approaches for n>p to be adapted to n
ISSN:1369-7412
1467-9868
DOI:10.1111/rssb.12093