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Robust Statistics (Wiley Series in Probability and Statistics), by Peter J. Huber
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The first systematic, book-length treatment of the subject. Begins with a general introduction and the formal mathematical background behind qualitative and quantitative robustness. Stresses concepts. Provides selected numerical algorithms for computing robust estimates, as well as convergence proofs. Tables contain quantitative robustness information for a variety of estimates.
- Sales Rank: #3939114 in Books
- Published on: 1981-02
- Original language: English
- Number of items: 1
- Dimensions: 9.21" h x .83" w x 6.22" l, .0 pounds
- Binding: Hardcover
- 320 pages
Review
“…informative and worth the effort for anyone with an interest in this field...” (ISCB News 38, December 2004)
From the Publisher
The first systematic, book-length treatment of the subject. Begins with a general introduction and the formal mathematical background behind qualitative and quantitative robustness. Stresses concepts. Provides selected numerical algorithms for computing robust estimates, as well as convergence proofs. Tables contain quantitative robustness information for a variety of estimates.
From the Inside Flap
Although several leading scientists in the late nineteenth and early twentieth centuries possessed a clear, operational understanding of the idea of robust statistics, the field was not recognized as a legitimate area of investigation until the mid-1960s. Briefly, a statistical method that exhibits an insensitivity to deviation from its own assumptions, is robust. The present volume represents the first systematic, book-length exposition of the subject. The treatment here is theoretical, with the stress on concepts rather than on extensive mathematical completeness. Chapter 1 provides a general introduction and overview. Chapter 2 contains an account of the formal mathematical background behind qualitative and quantitative robustness. Chapter 3 introduces the M-, L-, and R-estimates, and Chapter 4 treats the asymptotic minimax theory for location estimates. Chapters 5 to 11 branch out in different directions and are basically self-contained, covering scale estimates, multiparameter problems, regression, robust covariance and correlation matrices, robustness of design, exact finite sample results, and miscellaneous topics. The text describes selected numerical algorithms for computing robust estimates, provides convergence proofs where possible, and includes numerous tables with quantitative robustness information for a variety of estimates. Robust Statistics reorganizes, summarizes and extends a wealth of material only partially available in published form, providing a solid foundation in robustness for statisticians, mathematicians and graduate students.
Most helpful customer reviews
29 of 31 people found the following review helpful.
classic early text on robust methods
By Michael R. Chernick
In the 1970s Peter Huber was one of the innovative geniuses that developed the area of robust statistical methods. After the famous Princeton robustness study that Huber participated in there was a scattered set of techniques that were shown to be robust estimators of location based on simulations over wide classes of probability distributions. Huber and Hampel were the leaders at putting together some mathematical theory for robustness.
This book was the first attempt to unify the mathematical ideas into a general theory. It is intended for research statisticians and is a masterpiece for the subject. There are now other good books of a more practical nature. Huber also wrote a nice monograph in the SIAM series around the same time. It is now 20 years since the publication of the book and it perhaps deserves to be recognized by republication as a Wiley Classic.
8 of 9 people found the following review helpful.
Solid foundation in robust statistics
By Sonny
If you need clear explanations about robust statistics, if you need ideas to perform robust regression, or if you need some ground to develop robust algorithms, all you need is this text, and only this text. It covers theoretical as well as practical aspects of robust statistics. If you need more modern theoretical materials on robust statistics, Rieder's Asymptotic Robust Statistics is the companion text.
3 of 3 people found the following review helpful.
Professor Huber really owns this subject
By Kevin Gillette
Professor Huber has established himself as one of the titans of robust statistics, with numerous texts and monographs on the subject spanning multiple decades. With this 2nd edition of his comprehensive look at what it means for a statistic to be robust, Prof. Huber helps the mathematical statistician dive deeply into formal algebraic descriptions of robustness that are broadly applicable.
A small word of caution: This text assumes at least one-semester graduate-level introductions to real analysis and topology; otherwise, many of the discussions even in Chapter 1 will be mystifying. But if a little bit of weak(-star) topology talk doesn't faze you, then you're in for a treat!
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