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Regression and the Moore-Penrose pseudoinverse
Matrix inversion. --- Regression analysis. --- Regression analysis --- Matrix inversion --- Mathematical Statistics --- Mathematics --- Physical Sciences & Mathematics --- Analysis, Regression --- Linear regression --- Regression modeling --- Inverse matrices --- Inverse of a matrix --- Inversion, Matrix --- Multivariate analysis --- Structural equation modeling --- Linear operators --- Matrices --- Generalized inverses --- #WWIS:STAT --- 519.233 --- 519.233 Parametric methods --- Parametric methods --- Pseudoinverses --- Pseudo-inverses. --- Analyse de régression --- Inversion --- ELSEVIER-B EPUB-LIV-FT --- Regression Analysis
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One-dimensional quantum systems show fascinating properties beyond the scope of the mean-field approximation. However, the complicated mathematics involved is a high barrier to non-specialists. Written for graduate students and researchers new to the field, this book is a self-contained account of how to derive the exotic quasi-particle picture from the exact solution of models with inverse-square interparticle interactions. The book provides readers with an intuitive understanding of exact dynamical properties in terms of exotic quasi-particles which are neither bosons nor fermions. Powerful concepts, such as the Yangian symmetry in the Sutherland model and its lattice versions, are explained. A self-contained account of non-symmetric and symmetric Jack polynomials is also given. Derivations of dynamics are made easier, and are more concise than in the original papers, so readers can learn the physics of one-dimensional quantum systems through the simplest model.
Electronic structure --- Matrix inversion. --- Many-body problem. --- n-body problem --- Problem of many bodies --- Problem of n-bodies --- Mechanics, Analytic --- Inverse matrices --- Inverse of a matrix --- Inversion, Matrix --- Linear operators --- Matrices --- Structure, Electronic --- Atomic structure --- Energy-band theory of solids --- Mathematical models. --- Generalized inverses
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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 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.
Mathematical statistics. --- Matrices. --- Multivariate analysis. --- Multivariate analysis -- Problems, exercises, etc. --- Singular value decomposition --- Matrix inversion --- Algebras, Linear --- Mathematics --- Physical Sciences & Mathematics --- Algebra --- Mathematical Statistics --- Decomposition method. --- Matrix inversion. --- Algebras, Linear. --- Linear algebra --- Inverse matrices --- Inverse of a matrix --- Inversion, Matrix --- Method, Decomposition --- Algebra, Matrix --- Cracovians (Mathematics) --- Matrix algebra --- Matrixes (Algebra) --- Statistics. --- Statistics, general. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Algebra, Universal --- Generalized spaces --- Mathematical analysis --- Calculus of operations --- Line geometry --- Topology --- Linear operators --- Matrices --- Operations research --- Programming (Mathematics) --- System analysis --- Algebra, Abstract --- Generalized inverses --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Statistics .
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