Yanai, Haruo; Takeuchi, Kei; Takane, Yoshio Projection matrices, generalized inverse matrices, and singular value decomposition. (English) Zbl 1279.15003 Statistics for Social and Behavioral Sciences. New York, NY: Springer (ISBN 978-1-4419-9886-6/hbk; 978-1-4419-9887-3/ebook). xi, 234 p. (2011). The book under review is devoted, mainly, to projections and singular value decomposition (SVD). The text consists of six chapters and starts with Chapter 1 (Fundamentals of linear algebra) that establishes notations and introduces preliminaries from linear algebra. Chapter 2 (Projection matrices) plays an important role and is devoted to oblique projectors whereas the more commonly used orthogonal projectors are special cases of them. A particular attention is given to decompositions of projectors and corresponding subspaces. These geometrical considerations play an important role in Chapter 3 (General inverse matrices) and in Chapter 4 (Explicit representations). Chapter 5 (Singular value decomposition (SVD)) deals with connections between SVD, projectors and general inverse matrices. In the concluding Chapter 6 (Various applications) the authors demonstrate that the concepts given in the preceding chapters play important roles in applied fields such as linear regression analysis (the method of least squares and multiple regression analysis; multiple correlation coefficients and their partitions; the Gauss-Markov model), analysis of variance (one-way, two-way and three-way design; Cochran’s theorem), multivariate analysis (canonical correlation and canonical determination analysis; principal component analysis; distance and projection matrices), linear simultaneous equations (QR decomposition by the Gram-Schmidt orthogonalization method; QR decomposition by the Householder transformation; decomposition by projectors). Each chapter has some exercises. Many examples illustrate the presented material very well. The book should serve as a useful reference on projectors, general inverses and SVD, it is of interest to those working in matrix analysis, it can be recommended for graduate students as well as for professionals. Reviewer: Edward L. Pekarev (Odessa) Cited in 1 ReviewCited in 30 Documents MSC: 15-01 Introductory exposition (textbooks, tutorial papers, etc.) pertaining to linear algebra 15A18 Eigenvalues, singular values, and eigenvectors 15A23 Factorization of matrices 15B57 Hermitian, skew-Hermitian, and related matrices 00A06 Mathematics for nonmathematicians (engineering, social sciences, etc.) 15A09 Theory of matrix inversion and generalized inverses 62J10 Analysis of variance and covariance (ANOVA) 62H20 Measures of association (correlation, canonical correlation, etc.) 62J05 Linear regression; mixed models 62H35 Image analysis in multivariate analysis 65F20 Numerical solutions to overdetermined systems, pseudoinverses 65F25 Orthogonalization in numerical linear algebra Keywords:matrix analysis; eigenvalues; singular values; matrix factorization; singular value decomposition; least squares solution; generalized inverse; Moore-Penrose inverse; QR decomposition; canonical correlation analysis; canonical discriminant analysis; analysis of variance; exercises; textbook; linear regression; multiple regression; Gauss-Markov model; principal component analysis; Gram-Schmidt orthogonalization; Householder transformation PDFBibTeX XMLCite \textit{H. Yanai} et al., Projection matrices, generalized inverse matrices, and singular value decomposition. New York, NY: Springer (2011; Zbl 1279.15003) Full Text: DOI