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.
General
| Imprint: |
Springer International Publishing AG
|
| Country of origin: |
Switzerland |
| Series: |
Springer Texts in Statistics |
| Release date: |
October 2023 |
| First published: |
2023 |
| Authors: |
James E. Gentle
|
| Dimensions: |
254 x 178mm (L x W) |
| Edition: |
3rd ed. 2023 |
| ISBN-13: |
978-3-03-142143-3 |
| Categories: |
Books
Promotions
|
| LSN: |
3-03-142143-4 |
| Barcode: |
9783031421433 |
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