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Aside from distribution theory, projections and the singular value decomposition (SVD) are the two most important concepts for understanding the basic mechanism of multivariate analysis. The former underlies the least squares estimation in regression analysis, which is essentially a projection of one subspace onto another, and the latter underlies principal component analysis, which seeks to find a subspace that captures the largest variability in the original space. This book is about projections and SVD. A thorough discussion of generalized inverse (g-inverse) matrices is also given because...

This valuable reference on projectors, generalized inverses, and SVD covers concepts numerous cutting-edge concepts and provides systematic and in-depth accounts of these ideas from the viewpoint of linear transformations of finite dimensional vector spaces.