Reviews:
'Both statisticians and practitioners new to the analysis
of proximities should start with this well-organised and interesting
monograph. Misprints are infrequent and the general production
quality is excellent. Though not inexpensive, the book represents
good value and could serve as a textbook in a short course.
' Journal of the American Statistical Association
'... a useful introduction ...' The Statistician
'I strongly recommend this book to all practitioners of proximity
data and mulitdimensional scaling methods.' Technometrics
'The main strengths of this book are undoubtedly the clarity
of the exposition and the large number of examples.' Statistics
in Medicine
Key Features:
- The only comprehensive reference on the topic
- Clearly written – makes the theory accessible
- Large number of useful examples.
Description:
Proximity data consist of measures of similarity or dissimilarity
between members of a set of stimuli, individuals or objects
of interest, and occur in many different disciplines, particularly
psychology, sociology and market research. In some instances
such data arise from calculations carried out on the usual
multivariate data matrix, the elements of which record the
values of a number of variables on a number of individuals.
In other circumstances, proximity data are collected directly
from experiments in which human subjects are asked to make
judgements about the similarity or dissimilarity of pairs
of stimuli. Uncovering the pattern or structure in this type
of data may be important for a number of reasons, in particular
for discovering the dimensions on which similarity judgements
are made.
In this text a variety of methods which are helpful in investigating
and exploring proximity data are described and their use illustrated
on a range of data sets. Our hope is that the material contained
in the book will be a helpful introduction to this area both
for research workers who are not primarily statisticians but
who collect and wish to analyse proximity data, and to applied
statisticians interested in the underlying methodology.
Readership:
Statisticians in academia and industry; research workers
in a range of application areas, especially psychology and
medicine.
Contents:
Preface
1. Proximity data
2. Measures of similarity, dissimilarity and distance
3. Spatial representation of proximity data: metric and nonmetric
multidimensional scaling
4. Interpreting, diagnosing and comparing multidimensional
scaling solutions
5. Three-way multidimensional scaling
6. Asymmetric and rectangular data
7. Tree models for proximity data
Appendix A Distances in classical multivariate analysis
Appendix B Software for multidimensional scaling
References
Author index
Subject index
Links:
The authors’ home pages can be found at
http://www.iop.kcl.ac.uk/IoP/Departments/BioComp/brianpg.stm
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