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WILEY-INTERSCIENCE PAPERBACK SERIES
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.
"The writing style is clear and informal, and much of the discussion is oriented to application. In short, the book is a keeper."
–Mathematical Geology
"I would highly recommend the addition of this book to the libraries of both students and professionals. It is a useful textbook for the graduate student, because it emphasizes both the philosophy and practice of robustness in regression settings, and it provides excellent examples of precise, logical proofs of theorems. . . .Even for those who are familiar with robustness, the book will be a good reference because it consolidates the research in high-breakdown affine equivariant estimators and includes an extensive bibliography in robust regression, outlier diagnostics, and related methods. The aim of this book, the authors tell us, is ‘to make robust regression available for everyday statistical practice.’ Rousseeuw and Leroy have included all of the necessary ingredients to make this happen."
–Journal of the American Statistical Association
- Sales Rank: #872831 in Books
- Published on: 1987-10
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .90" w x 6.30" l, 1.10 pounds
- Binding: Hardcover
- 352 pages
Review
"…a wonderful book about methods of identifying outliers and then developing robust regression." (Journal of Statistical Computation and Simulation, July 2005)
From the Publisher
Provides an applications-oriented introduction to robust regression and outlier detection, emphasising high-breakdown methods which can cope with a sizeable fraction of contamination. Its self-contained treatment allows readers to skip the mathematical material which is concentrated in a few sections. Exposition focuses on the least median of squares technique, which is intuitive and easy to use, and many real-data examples are given. Chapter coverage includes robust multiple regression, the special case of one-dimensional location, algorithms, outlier diagnostics, and robustness in related fields, such as the estimation of multivariate location and covariance matrices, and time series analysis.
From the Back Cover
WILEY-INTERSCIENCE PAPERBACK SERIES
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.
"The writing style is clear and informal, and much of the discussion is oriented to application. In short, the book is a keeper."
–Mathematical Geology
"I would highly recommend the addition of this book to the libraries of both students and professionals. It is a useful textbook for the graduate student, because it emphasizes both the philosophy and practice of robustness in regression settings, and it provides excellent examples of precise, logical proofs of theorems. . . .Even for those who are familiar with robustness, the book will be a good reference because it consolidates the research in high-breakdown affine equivariant estimators and includes an extensive bibliography in robust regression, outlier diagnostics, and related methods. The aim of this book, the authors tell us, is ‘to make robust regression available for everyday statistical practice.’ Rousseeuw and Leroy have included all of the necessary ingredients to make this happen."
–Journal of the American Statistical Association
Most helpful customer reviews
27 of 29 people found the following review helpful.
great book on an interesting topic
By Michael R. Chernick
These authors provide an excellent guide to the available theory of robust regression. Graphical methods are well used to bring home the points. I have always liked looking at outliers and robustness from an influence function viewpoint and the authors do an excellent job of describing that theory.
The book came out in 1987 and so it is a little dated. Another reviewer criticizes some of the software that is mentioned. With computer methodology advancing so rapidly one should not expect the state-of-the-art in computing from a 14 year old text. Value it for its exposition and development of the theory.
18 of 19 people found the following review helpful.
Easy to follow introduction
By A Customer
Why should I read this book?
- The introduction alone is worth half the price of this book. It gives you a very good overview of robust estimators like m-estimators, least median of squares and least trimmed squares. Basically, you could just take the introduction and start implementing, if that's what you want. The author is able to tell how this and that method overcomes a problem and for what price, and all in one paragraph.
What do I need to understand this book?
- The math in this book is kept at an engineer's level, so an introductory course on statistics should be enough to follow the introduction and to get an insight into the following chapters. A lot of examples with real life data and nice plots show how the methods perform.
What's the drawback?
- The author talks a lot about his old-school fortran program called 'PROGRESS' - and eventually comes up with screen dumps (when people were still using terminals) of the program output. He even gives usage information like 'PROGRESS is designed to run on an IBM-PC or a compatible microcomputer. At least 256K RAM must be available.' Neither the source nor the binary is shipped with the book.
- Some new trends in robust statistics are not included, in particular, I do miss the RANSAC.
Conclusion:
- Better get this book from a library and photocopy the introduction. If you're not really interested, the introductory chapter should be enough.
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