Common principal components and related multivariate models
著者
書誌事項
Common principal components and related multivariate models
（Wiley series in probability and mathematical statistics, . Applied probability and statistics）
Wiley, c1988
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注記
Bibliography: p. 245-252
Includes indexes
内容説明・目次
内容説明
Recent developments in the theory of principal component analysis have led to generalizations of 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. It 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.
目次
- Preliminaries
- principal component analysis
- relationships between matrices
- common practical components
- proportional covariance matrices
- partial common components and common space analysis
- how different are several covariance matrices?
- numerical methods. Appendices: spectral decomposition of symmetric matrices
- some results from matrix algebra
- a fortran program for the fg algorithm.
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