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Asymptotic theory for the principal component analysis of a vector random function: Some applications to statistical inference. (English) Zbl 0539.62064
Summary: 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 Factor analysis and principal components; correspondence analysis
62E20 Asymptotic distribution theory in statistics
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