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Theory of Statistics

Theory of Statistics BOOK REVIEWS the question 'Why multivariate analysis?', and Chapter 2 is over 40 pages of largely standard matrix algebra.) I began Chapter 3, 'Characterizing and displaying multivariate data', with the expectation that Dr Rencher would demonstrate, primarily with graphical methods, how to obtain an initial look beneath that tangled web of variables so poetically described in the preface, to extract the patterns in a complex set of multivariate data. But rather than an extravaganza of scatterplot matrices, three­ dimensional plots, discussions of 'brushing' and 'spinning' (even perhaps some examples of the application of Chernoff faces as a little light relief), the chapter consists largely of a very straightforward account of covariances and correlations with only minimal graphical material. And Chapter 3 sets the style for subsequent chapters covering the multivariate normal distribution, tests on mean vectors, test on covariance matrices and multivariate analysis of variance. Each topic is well described, but in a completely standard formal inferential framework that could have been written more than two decades ago. Even the chapters describing discriminant analysis, principal components analysis and factor analysis retain the formal flavour of what might be termed the 'classical' approach. The topics missing from Dr Rencher's account http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the Royal Statistical Society Series D: The Statistician Oxford University Press

Theory of Statistics

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Publisher
Oxford University Press
Copyright
© 1996 Royal Statistical Society
ISSN
2515-7884
eISSN
1467-9884
DOI
10.2307/2988561
Publisher site
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Abstract

BOOK REVIEWS the question 'Why multivariate analysis?', and Chapter 2 is over 40 pages of largely standard matrix algebra.) I began Chapter 3, 'Characterizing and displaying multivariate data', with the expectation that Dr Rencher would demonstrate, primarily with graphical methods, how to obtain an initial look beneath that tangled web of variables so poetically described in the preface, to extract the patterns in a complex set of multivariate data. But rather than an extravaganza of scatterplot matrices, three­ dimensional plots, discussions of 'brushing' and 'spinning' (even perhaps some examples of the application of Chernoff faces as a little light relief), the chapter consists largely of a very straightforward account of covariances and correlations with only minimal graphical material. And Chapter 3 sets the style for subsequent chapters covering the multivariate normal distribution, tests on mean vectors, test on covariance matrices and multivariate analysis of variance. Each topic is well described, but in a completely standard formal inferential framework that could have been written more than two decades ago. Even the chapters describing discriminant analysis, principal components analysis and factor analysis retain the formal flavour of what might be termed the 'classical' approach. The topics missing from Dr Rencher's account

Journal

Journal of the Royal Statistical Society Series D: The Statistician – Oxford University Press

Published: Dec 5, 2018

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