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.

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Year of publication:	2009
Authors:	Salinelli, Ernesto
Published in:	Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 100.2009, 4, p. 652-660
Publisher:	Elsevier
Subject:	primary \| 62H25 secondary \| 49R50 Nonlinear principal components Normal distributions Chernoff inequality Hermite polynomials

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10005153306