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Recent developments in the theory of principal component analysis have led to generalizations to cases where data fall in natural groups. This book offers for the first time a comprehensive view of the topics presented--the mathematical theory, applications to real data, and computational techniques. Treats both the classical method and recent generalizations, including the model of proportional covariance matrices, and the common principal component model and its variations. Methods are illustrated by numerical examples based on real data. The book should appeal to both mathematical and applied statisticians, and numerical analysts will appreciate the material on simultaneous diagonalization of symmetric matrices.
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