Asymptotic theory for the principal component analysis of a vector random function: Some applications to statistical inference

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Abstract

From the results of convergence by sampling in linear principal component analysis (of a random function in a separable Hilbert space), the limiting distribution is given for the principal values and the principal factors. These results can be explicitly written in the normal case. Some applications to statistical inference are investigated.

MSC

62H25

Keywords

Principal component analysis
asymptotic distributions

Cited by (0)

UER de Mathématiques, Informatique, Statistique et Sciences Expérimentales, Université de Toulouse-Le Mirail, France.

Département de Mathématiques, Université de Pau et des pays de l'Adour, France.