An introduction to multivariate statistical analysis

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Preface to the Third Edition
For some forty years the first and second editions of this book have been
used by students to acquire a basic knowledge of the theory and methods of
multivariate statistical analysis. The book has also served a wider community
of statisticians in furthering their understanding and proficiency in this field.
Since the second edition was published, multivariate analysis has been
developed and extended in many directions. Rather than attempting to cover,
or even survey, the enlarged scope, I have elected to elucidate several aspects
that are particularly interesting and useful for methodology and comprehension.
Earlier editions included some methods that could be carried out on an
adding machine! In the twenty-first century, however, computational techniques
have become so highly developed and improvements come so rapidly
that it is impossible to include all of the relevant methods in a volume on the
general mathematical theory. Some aspects of statistics exploit computational
power such as the resampling technologies; these are not covered here.
The definition of multivariate statistics implies the treatment of variables
that are interrelated. Several chapters are devoted to measures of correlation
and tests of independence. A new chapter, "Patterns of Dependence; Graphical
Models" has been added. A so-called graphical model is a set of vertices
or nodes identifying observed variables together with a new set of edges
suggesting dependences between variables. The algebra of such graphs is an
outgrowth and development of path analysis and the study of causal chains.
A graph may represent a sequence in time or logic and may suggest causation
of one set of variables by another set.
Another new topic systematically presented in the third edition is that of
elliptically contoured distributions. The multivariate normal distribution,
which is characterized by the mean vect.or and covariance matrix, has a
limitation that the fourth-order moments of the variables are determined by
the first- and second-order moments. The class .of elliptically contoured
distribution relaxes this restriction. A density in this class has contours of
equal density which are ellipsoids as does a normal density, but the set of
fourth-order moments has one. further degree of freedom. This topic is
expounded by the addition of sections to appropriate chapters.
Reduced rank regression developed in Chapters 12 and 13 provides a
method of reducing the number of regression coefficients to be estimated in
the regression of one set of variables to another. This approach includes the
limited-information maximum-likelihood estimator of an equation in a simultaneous
equations model.

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فرمت کتاب

ebook

زبان

english

تاریخ انتشار

2003

نویسنده

Anderson T.W

ناشر

Wiley

شابک

1110538103

20,000 تومان

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خرید کتاب An introduction to multivariate statistical analysis

An introduction to multivariate statistical analysis

گزارش تخلف

فرمت کتاب

ebook

زبان

english

تاریخ انتشار

2003

نویسنده

ناشر

شابک

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