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.
Robust Statistics
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Book Description
Editorial Reviews
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"A comprehensive introduction and discussion on the formal mathematical background behind qualitative and quantitative robustness is provided, and subsequent chapters delve into basic types of scale estimates, asymptotic minimax theory, regression, robust covariance, and robust design . . . it also serves as a valuable reference for researchers and practitioners who wish to study the statistical research associated with robust statistics" (Mathematical Reviews, 2010)
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.
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Most Helpful Customer Reviews
29 of 31 people found the following review helpful:
5.0 out of 5 stars
classic early text on robust methods,
By
This review is from: Robust Statistics (Wiley Series in Probability and Statistics) (Hardcover)
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.
7 of 8 people found the following review helpful:
5.0 out of 5 stars
Solid foundation in robust statistics,
By Sonny (Australia) - See all my reviews
This review is from: Robust Statistics (Wiley Series in Probability and Statistics) (Hardcover)
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.
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Inside This Book
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First Sentence:
Statistical inferences are based only in part upon the observations. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
minimax tests, qualitative robustness, expected sample size, influence curve, median absolute deviation, location estimates, asymptotic variance, breakdown point, robust procedures, robust estimation Key Phrases - Capitalized Phrases (CAPs): (learn more)
Proof Assume, Proof Let, Monte Carlo New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Statistical inferences are based only in part upon the observations. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
minimax tests, qualitative robustness, expected sample size, influence curve, median absolute deviation, location estimates, asymptotic variance, breakdown point, robust procedures, robust estimation Key Phrases - Capitalized Phrases (CAPs): (learn more)
Proof Assume, Proof Let, Monte Carlo New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 100
books:
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100
books
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- Stochastic Geometry (Wiley Series in Probability & Statistics) by Robert Harding in Back Matter (1), and Back Matter (2)
- Data Analysis (New Topics for Secondary School Mathematics) by Nc School of Sci and Math in Back Matter (1), and Back Matter (2)
- Time Series (Charles Griffin Book) by Maurice G. Kendall in Back Matter (1), and Back Matter (2)
- An Introduction to Probability Theory and Its Applications, Vol. 1, 3rd Edition by William Feller in Back Matter (1), and Back Matter (2)
- Discriminant Analysis and Statistical Pattern Recognition (Wiley Series in Probability and Statistics) by Geoffrey J. McLachlan in Back Matter
See all 100 books this book cites
- Robust Statistical Procedures: Asymptotics and Interrelations by Jana Jurecková in Back Matter (1), and Back Matter (2)
- Finite Population Sampling and Inference: A Prediction Approach (Wiley Series in Survey Methodology) by Richard Valliant in Back Matter (1), and Back Matter (2)
- Discriminant Analysis and Statistical Pattern Recognition by Geoffrey J. McLachlan in Front Matter, and Back Matter
- Signal Processing Noise (Electrical Engineering & Applied Signal Processing Series) by Vyacheslav Tuzlukov on page 108, and page 300
- Advances in Econometrics: Volume 1: Sixth World Congress (Econometric Society Monographs) by Christopher A. Sims on page 169, and page 208
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