A generalized inverse of a linear transformation A: v yield w, where v and w are finite dimensional vector spaces, is defined using geometric concepts of linear transformations and projection operators. The inverse is uniquely defined in terms of specified subspaces m is a subset of v, 1 is a subset of w and a linear transformation N such that AN = O, NA = O. Such an inverse which is unique is called the 1mN-inverse. A Moore-Penrose type inverse is obtained by putting N=O. Applications to optimization problems when v and w are inner product spaces, such as least squares in a general setting, are discussed. The results given in the paper can be extended without any major modification of proofs to bounded linear operators with closed range on Hilbert spaces. Keywords: G inverse; Linear transformation; Moore Penrose inverse; Projection operator. (jhd).

This Is The Fourth Volume Of Selected Papers Of C. R. Rao Consisting Of 39 Papers Published During 1975-1985. These Papers Represent The Development Of Some Of The Basic Concepts Proposed By The Author In The Fields Of Unified Theory Of Least Squares Estimation, Weighted Distributions, Bayesian Statistical Inference, Generalised Inverses Of Matrices And Their Applications In Which Contemporary Research Is Carried Out Extensively. Work On Solutions Of Functional Equations And Their Application In Characterizations Of Distributions Is Also Of Current Interest. Introduction Of Measures Of Diversity, Quadratic Entropy And Allied Concepts Find Applications In Various Fields Such As Anthropology And Social Sciences. As In The Earlier Volumes, The Papers That Have Originally Appeared In Different Publications Have Been Retypeset To Maintain Uniformity In Presentation.The Final Volume With More Papers, An Updated Bibliography Of Works And A Comprehensive Overview Of The Total Opus Of Professor C. R. Rao Is Going To Come Out Soon.

This second edition accounts for many major developments in generalized inverses while maintaining the informal and leisurely style of the 1974 first edition. Added material includes a chapter on applications, new exercises, and an appendix on the work of E.H. Moore.

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 it is closely related to the former. The book provides systematic and in-depth accounts of these concepts from a unified viewpoint of linear transformations finite dimensional vector spaces. More specially, it shows that projection matrices (projectors) and g-inverse matrices can be defined in various ways so that a vector space is decomposed into a direct-sum of (disjoint) subspaces. Projection Matrices, Generalized Inverse Matrices, and Singular Value Decomposition will be useful for researchers, practitioners, and students in applied mathematics, statistics, engineering, behaviormetrics, and other fields.

For linear operators A, B on a beta-space, if B approximates A suitably then the generalized inverse of A is approximated by (B sub phi), related to but in general different from the generalized inverse of B. Applications are for numerical solutions of integral equations and approximate least squares solutions. Known matrix results are recovered. (Author).