Nonlinear principal components, II: Characterization of normal distributions

Ernesto Salinelli

Journal of Multivariate Analysis, 2009, vol. 100, issue 4, 652-660

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.

Keywords: primary; 62H25 secondary; 60E05; 47A75; 49R50 Nonlinear principal components Normal distributions Chernoff inequality Hermite polynomials (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (2)

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