Description

Matrix algebra is one of the most important areas of mathematics for data analysis and for statistical theory. This much-needed work presents the relevant aspects of the theory of matrix algebra for applications in statistics. It moves on to consider the various types of matrices encountered in statistics, such as projection matrices and positive definite matrices, and describes the special properties of those matrices. Finally, it covers numerical linear algebra, beginning with a discussion of the basics of numerical computations, and following up with accurate and efficient algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors.

Matrix Algebra: Theory, Computations and Applications in Statistics

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£99.99

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Paperback / softback by James E. Gentle

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Matrix algebra is one of the most important areas of mathematics for data analysis and for statistical theory. This much-needed... Read more

    Publisher: Springer International Publishing AG
    Publication Date: 21/10/2017
    ISBN13: 9783319648668, 978-3319648668
    ISBN10: 3319648667

    Number of Pages: 648

    Non Fiction , Mathematics & Science , Education

    Description

    Matrix algebra is one of the most important areas of mathematics for data analysis and for statistical theory. This much-needed work presents the relevant aspects of the theory of matrix algebra for applications in statistics. It moves on to consider the various types of matrices encountered in statistics, such as projection matrices and positive definite matrices, and describes the special properties of those matrices. Finally, it covers numerical linear algebra, beginning with a discussion of the basics of numerical computations, and following up with accurate and efficient algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors.

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