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-Poincare type inequalities and their applications to the characterization of normal distributions.
Nonlinear Principal Components II. Characterization of Normal Distributions
SALINELLI, Ernesto
2009-01-01
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-Poincare type inequalities and their applications to the characterization of normal distributions.File in questo prodotto:
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