Theory of Multivariate Statistics

Overview

Intended as a textbook for students taking a first graduate course in the subject, as well as for the general reference of interested research workers, this text discusses, in a readable form, developments from recently published work on certain broad topics not otherwise easily accessible, such as robust inference and the use of the bootstrap in a multivariate setting. A minimum background expected of the reader would include at least two courses in mathematical statistics, and certainly some exposure to the ...

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Paperback (Softcover reprint of the original 1st ed. 1999)
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Overview

Intended as a textbook for students taking a first graduate course in the subject, as well as for the general reference of interested research workers, this text discusses, in a readable form, developments from recently published work on certain broad topics not otherwise easily accessible, such as robust inference and the use of the bootstrap in a multivariate setting. A minimum background expected of the reader would include at least two courses in mathematical statistics, and certainly some exposure to the calculus of several variables together with the descriptive geometry of linear algebra.

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Editorial Reviews

From the Publisher
"This is an excellent graduate level textbook with several challenging problems in the exercises. An outstanding feature of the book is the presentation style. The authors' presentations of core statistical ideas, important formulae, the scope and the limitations of the topics create a curiosity to continue reading. ... I enjoyed reading this book and learned a lot!"
Short Book Reviews, Vol. 20/2, August 2000
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Product Details

  • ISBN-13: 9781475773033
  • Publisher: Springer New York
  • Publication date: 5/28/2013
  • Series: Springer Texts in Statistics Series
  • Edition description: Softcover reprint of the original 1st ed. 1999
  • Pages: 290
  • Product dimensions: 6.14 (w) x 9.21 (h) x 0.66 (d)

Table of Contents

Linear algebra.- Random vectors.- Gamma, Dirichlet, and F distributions.- Invariance.- Multivariate normal.- Multivariate sampling.- Wishart distributions.- Tests on mean and variance.- Multivariate regression.- Principal components.- Canonical correlations.- Asymptotic expansions.- Robustness.- Bootstrap confidence regions and tests.

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