Applied Matrix Algebra in the Statistical Sciences (Paperback)

Alexander Basilevsky

ISBN 10: 0486445380 / ISBN 13: 9780486445380

Editore: Dover Publications Inc., New York, 2006

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Paperback. This comprehensive text covers both applied and theoretical branches of matrix algebra in the statistical sciences. It also provides a bridge between linear algebra and statistical models. Appropriate for advanced undergraduate and graduate students, the self-contained treatment also constitutes a handy reference for researchers. The only mathematical background necessary is a sound knowledge of high school mathematics and a first course in statistics.Consisting of two interrelated parts, this volume begins with the basic structure of vectors and vector spaces. The latter part emphasizes the diverse properties of matrices and their associated linear transformations--and how these, in turn, depend upon results derived from linear vector spaces. An overview of introductory concepts leads to more advanced topics such as latent roots and vectors, generalized inverses, and nonnegative matrices. Each chapter concludes with a section on real-world statistical applications, plus exercises that offer concrete examples of the applications of matrix algebra. This comprehensive text offers teachings relevant to both applied and theoretical branches of matrix algebra and provides a bridge between linear algebra and statistical models. Appropriate for advanced undergraduate and graduate students. 1983 edition. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Codice articolo 9780486445380

Dati bibliografici

• Titolo: Applied Matrix Algebra in the Statistical Sciences (Paperback)
• Editore: Dover Publications Inc., New York
• Data di pubblicazione: 2006
• Lingua: Inglese
• ISBN 10: 0486445380
• ISBN 13: 9780486445380
• Rilegatura: Paperback
• Condizione: new

Descrizione articolo

Riassunto

This comprehensive text covers both applied and theoretical branches of matrix algebra in the statistical sciences. It also provides a bridge between linear algebra and statistical models. Appropriate for advanced undergraduate and graduate students, the self-contained treatment also constitutes a handy reference for researchers. The only mathematical background necessary is a sound knowledge of high school mathematics and a first course in statistics.
Consisting of two interrelated parts, this volume begins with the basic structure of vectors and vector spaces. The latter part emphasizes the diverse properties of matrices and their associated linear transformations--and how these, in turn, depend upon results derived from linear vector spaces. An overview of introductory concepts leads to more advanced topics such as latent roots and vectors, generalized inverses, and nonnegative matrices. Each chapter concludes with a section on real-world statistical applications, plus exercises that offer concrete examples of the applications of matrix algebra.

Product Description

This comprehensive text covers both applied and theoretical branches of matrix algebra in the statistical sciences. It begins by dealing with the basic structure of vectors and vector spaces and then emphasizes the diverse properties of matrices and their associated linear transformations - and how these, in turn, depend upon results derived from linear vector spaces. 1983 edition.