Theory of Multivariate Statistics
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BeschreibungOur object in writing this book is to present the main results of the modern theory of multivariate statistics to an audience of advanced students who would appreciate a concise and mathematically rigorous treatment of that material. It is intended for use as a textbook by students taking a first graduate course in the subject, as well as for the general reference of interested research workers who will find, in a readable form, developments from recently published work on certain broad topics not otherwise easily accessible, as for instance robust inference (using adjusted likelihood ratio tests) 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.
InhaltsverzeichnisLinear 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.
Pressestimmen"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
Untertitel: 1999. Auflage. Book. Sprache: Englisch.
Erscheinungsdatum: August 1999
Seitenanzahl: 316 Seiten