Nonlinear principal components, II: Characterization of normal distributions

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.