Linear Algebra and Matrix Analysis for Statistics

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A01=Anindya Roy
A01=Sudipto Banerjee
Algorithms For Eigenvalues And Eigenvectors
Author_Anindya Roy
Author_Sudipto Banerjee
Category=PB
Computational Techniques For Orthogonal Reduction
Echelon Matrix
Elementary Row Operations
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Full Column Rank
Full Row Rank
Fundamental Subspaces
Fundamental Theorem Of Linear Algebra
Gauss Jordan Elimination
Gaussian Elimination
Hilbert Spaces
Kronecker And Hadamard Products
Linearly Independent
LU Decomposition
LU Factor
Nonnegative Definite
Nonsingular Matrix
Null Space
Orthogonal Projection
Orthonormal Basis
Permutation Matrix
QR Decomposition
Rank Nullity Theorem
Real Symmetric Matrix
Row Echelon Form
Row Interchanges
RREF
Spectral Decomposition
System Ax
Undergraduate Course In Linear Algebra
Vector Space

Product details

  • ISBN 9781420095388
  • Weight: 980g
  • Dimensions: 156 x 234mm
  • Publication Date: 06 Jun 2014
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Linear Algebra and Matrix Analysis for Statistics offers a gradual exposition to linear algebra without sacrificing the rigor of the subject. It presents both the vector space approach and the canonical forms in matrix theory. The book is as self-contained as possible, assuming no prior knowledge of linear algebra.

The authors first address the rudimentary mechanics of linear systems using Gaussian elimination and the resulting decompositions. They introduce Euclidean vector spaces using less abstract concepts and make connections to systems of linear equations wherever possible. After illustrating the importance of the rank of a matrix, they discuss complementary subspaces, oblique projectors, orthogonality, orthogonal projections and projectors, and orthogonal reduction.

The text then shows how the theoretical concepts developed are handy in analyzing solutions for linear systems. The authors also explain how determinants are useful for characterizing and deriving properties concerning matrices and linear systems. They then cover eigenvalues, eigenvectors, singular value decomposition, Jordan decomposition (including a proof), quadratic forms, and Kronecker and Hadamard products. The book concludes with accessible treatments of advanced topics, such as linear iterative systems, convergence of matrices, more general vector spaces, linear transformations, and Hilbert spaces.

University of Minnesota, Minneapolis, USA Department of Math and Statistics, University of Maryland Baltimore County, USA

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