此书是Rand R. Wilcox的作品（此人，想必大家熟悉），下面是本书的内容简介。
PREFACE
Overview
The goals in this book are: (1) to describe fundamental principles in a manner that
takes into account many new insights and advances that are often ignored in an
introductory course, (2) to summarize basic methods covered in a graduate level,
applied statistics course dealing with ANOVA, regression, and rank-based methods,
(3) to describe how and why conventional methods can be unsatisfactory, and
(4) to describe recently developed methods for dealing with the practical problems
associated with standard techniques. Another goal is to help make contemporary
techniques more accessible by supplying and describing easy-to-use S-PLUS functions.
Many of the S-PLUS functions included here have not appeared in any other
book. (Chapter 1 provides a brief introduction to S-PLUS so that readers unfamiliar
with S-PLUS can employ the methods covered in the book.) Problems with standard
statistical methods are well known among quantitative experts but are rarely
explained to students and applied researchers. The many details are simplified and
elaborated upon in a manner that is not available in any other book. No prior training
in statistics is assumed.
Features
The book contains many methods beyond those in any other book and provides
a much more up-to-date look at the strategies used to address nonnormality and
heteroscedasticity. The material on regression includes several estimators that have
recently been found to have practical value. Included is the deepest regression line
estimator recently proposed by Rousseeuw and his colleagues. The last chapter covers
rank-based methods, but unlike any other book, the latest information on handling
tied values is described. (Brunner and Cliff describe different strategies for dealing
with ties and both are considered.) Recent results on two-way designs are covered,
including repeated measures designs.
Chapter 7 provides a simple introduction to bootstrap methods, and chapters 8–14
include the latest information on the relative merits of different bootstrap techniques
when dealing with ANOVA and regression. The best non-bootstrap methods are
covered as well. Again, methods and advances not available in any other book are
described.
Chapters 13–14 include many new insights about robust regression that are not
available in any other book. For example, many estimators often provide substantial
improvements over ordinary least squares, but recently it has been found that some
of these estimators do not always correct commonly occurring problems. Improved
methods are covered in this book. Smoothers are described and recent results on
checking for linearity are included.
Acknowledgments
The author is grateful to Sam Green, Philip Ramsey, Jay Devore, E. D. McCune,
Xuming He, and Christine Anderson-Cook for their helpful comments on how to
improve this book. I am also grateful to Pat Goeters and Matt Carlton for their checks
on accuracy, but of course I am responsible for any remaining errors. I’m especially
grateful to Harvey Keselman for many stimulating conversations regarding this book
as well as inferential methods in general.
Rand R. Wilcox
Los Angeles, California
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