Rank bound
Theorem taught in High-dimensional analysis, part of Statistical learning
Rank bound
| Type | Theorem |
| Taught in | High-dimensional analysis |
| Part | Statistical learning |
\operatorname{rank}(S)\le\min(n-1,p). Hence S is invertible only if p\le n-1 and the centred data have rank p.
Used in
- Taught in High-dimensional analysis, where the derivation and examples are.
- All 9 questions on High-dimensional analysis
See also
Family-wise control implies false-discovery control · Benjamini–Hochberg · Benjamini–Hochberg controls the false discovery rate · Why classical methods fail when p > n · Rank of the sample covariance · Regression when p > n · Where high-dimensional columns come from · Questions to ask of high-dimensional data