| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 1147156 | Journal of Multivariate Analysis | 2009 | 9 Pages |
Abstract
Nonlinear principal components are defined for normal random vectors. Their properties are investigated and interpreted in terms of the classical linear principal component analysis. A characterization theorem is proven. All these results are employed to give a unitary interpretation to several different issues concerning the Chernoff–Poincaré type inequalities and their applications to the characterization of normal distributions.
Related Topics
Physical Sciences and Engineering
Mathematics
Numerical Analysis
Authors
Ernesto Salinelli,