Projection Matrices, Generalized Inverse Matrices, and Singular Value Decomposition

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 on...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Yanai, Haruo (Συγγραφέας), Takeuchi, Kei (Συγγραφέας), Takane, Yoshio (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: New York, NY : Springer New York, 2011.
Σειρά:Statistics for Social and Behavioral Sciences
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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100 1 |a Yanai, Haruo.  |e author. 
245 1 0 |a Projection Matrices, Generalized Inverse Matrices, and Singular Value Decomposition  |h [electronic resource] /  |c by Haruo Yanai, Kei Takeuchi, Yoshio Takane. 
264 1 |a New York, NY :  |b Springer New York,  |c 2011. 
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490 1 |a Statistics for Social and Behavioral Sciences 
505 0 |a Fundamentals of Linear Algebra -- Projection Matrices -- Generalized Inverse Matrices -- Explicit Representations -- Singular Value Decomposition (SVD) -- Various Applications. 
520 |a 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. 
650 0 |a Statistics. 
650 1 4 |a Statistics. 
650 2 4 |a Statistics, general. 
650 2 4 |a Statistics for Life Sciences, Medicine, Health Sciences. 
700 1 |a Takeuchi, Kei.  |e author. 
700 1 |a Takane, Yoshio.  |e author. 
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776 0 8 |i Printed edition:  |z 9781441998866 
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856 4 0 |u http://dx.doi.org/10.1007/978-1-4419-9887-3  |z Full Text via HEAL-Link 
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950 |a Mathematics and Statistics (Springer-11649)