Nonlinear principal components, II: Characterization of normal distributions

Ernesto Salinelli

doi:10.1016/j.jmva.2008.07.001

Nonlinear principal components, II: Characterization of normal distributions

Ernesto Salinelli

Dipartimento di Medicina Traslazionale

Risultato della ricerca: Contributo su rivista › Articolo in rivista › peer review

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.

Lingua originale	Inglese
pagine (da-a)	652-660
Numero di pagine	9
Rivista	Journal of Multivariate Analysis
Volume	100
Numero di pubblicazione	4
DOI	https://doi.org/10.1016/j.jmva.2008.07.001
Stato di pubblicazione	Pubblicato - apr 2009

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10.1016/j.jmva.2008.07.001

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title = "Nonlinear principal components, II: Characterization of normal distributions",

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-Poincar{\'e} type inequalities and their applications to the characterization of normal distributions.",

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KW - 60E05

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KW - Chernoff inequality

KW - Hermite polynomials

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KW - Normal distributions

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KW - secondary

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DO - 10.1016/j.jmva.2008.07.001

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Nonlinear principal components, II: Characterization of normal distributions

Abstract

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