Reviews:
'An invaluable source of reference.' Mathematical
Review
'The authors are to be congratulated. … 'There is an
excellent section on conditional independence models...this
updated text on multivariate analysis has been carefully researched
and provides a good source of information on this important
subject.' Mathematics Today
'...the authors succeed in providing useful introductions
to many approaches, quite a few of which are illustrated by
exemplary analyses of real data ... this book, together with
Part 1, is a valuable reference book, which will lead a statistician
to most important references on a multitude of subjects in
the field of multivariate analysis.' Journal of Classification
Key Features:
- Gives a comprehensive coverage of all technical aspects
of the subject
- Readable and user-friendly in its presentation and includes
numerous examples
- Uses exercises to illustrate the theory
- Fully referenced with a discussion of the available software.
Description:
This second of a comprehensive two-volume work on multivariate
analysis is concerned with the more specialised techniques
that follow on from the basic theory presented in Part
One.
Topics covered include discriminant analysis, cluster analysis,
path analysis, graphical modelling, latent variable techniques,
repeated measures analysis and growth curve models. Modern
problems and techniques, such as handling of high dimensional
data and the use of neural networks are featured and the book
concludes with a discussion of strategic aspects of multivariate
analysis.
Readership:
Graduate statisticians and researchers
Contents:
Preface
1. Discriminant analysis
2. Cluster analysis
3. Covariance and interaction structures
4. Latent structure models
5. Repeated measures and growth curves
6. Miscellaneous topics
7. Review: Strategic aspects and future prospects.
References
Author index
Subject index
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