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Basics of matrix algebra for statistics with R / Nick Fieller.

By: Material type: TextTextSeries: Chapman & Hall/CRC the R seriesPublication details: Boca Raton : CRC Press, ©2016.Description: xviii, 226 p. : illustrations ; 24 cmISBN:
  • 9781498712361
Subject(s): DDC classification:
  • 000SA.055 23 F463
Contents:
1. Introduction -- 2. Vectors and matrices -- 3. Rank of matrices -- 4. Determinats -- 5. Inverses -- 6. Eigenanalysis of real symmetric matrices -- 7. Vector and matrix calculus -- 8. Further topics -- 9. Key applications to statistics
Summary: The book introduces the definition of a matrix and the basic rules of addition, subtraction, multiplication, and inversion. Later topics include determinants, calculation of eigenvectors and eigenvalues, and differentiation of linear and quadratic forms with respect to vectors. The text explores how these concepts arise in statistical techniques, including principal component analysis, canonical correlation analysis, and linear modeling. In addition to the algebraic manipulation of matrices, the book presents numerical examples that illustrate how to perform calculations by hand and using R. Many theoretical and numerical exercises of varying levels of difficulty aid readers in assessing their knowledge of the material. Outline solutions at the back of the book enable readers to verify the techniques required and obtain numerical answers.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 000SA.055 F463 (Browse shelf(Opens below)) Available 137080
Total holds: 0

Includes bibliographical references and index.

1. Introduction --
2. Vectors and matrices --
3. Rank of matrices --
4. Determinats --
5. Inverses --
6. Eigenanalysis of real symmetric matrices --
7. Vector and matrix calculus --
8. Further topics --
9. Key applications to statistics

The book introduces the definition of a matrix and the basic rules of addition, subtraction, multiplication, and inversion. Later topics include determinants, calculation of eigenvectors and eigenvalues, and differentiation of linear and quadratic forms with respect to vectors. The text explores how these concepts arise in statistical techniques, including principal component analysis, canonical correlation analysis, and linear modeling.
In addition to the algebraic manipulation of matrices, the book presents numerical examples that illustrate how to perform calculations by hand and using R. Many theoretical and numerical exercises of varying levels of difficulty aid readers in assessing their knowledge of the material. Outline solutions at the back of the book enable readers to verify the techniques required and obtain numerical answers.

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