Robust Statistics, 2nd Edition 2nd Edition by Huber | (PDF) Free Download

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A new edition of the classic, groundbreaking book on robust statisticsOver twenty-five years after the publication of its predecessor, Robust Statistics, Second Edition continues to provide an authoritative and systematic treatment of the topic. This new edition has been thoroughly updated and expanded to reflect the latest advances in the field while also outlining the established theory and applications for building a solid foundation in robust statistics for both the theoretical and the applied statistician.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. In addition to an extended treatment of robust regression, the Second Edition features four new chapters covering:Robust TestsSmall Sample AsymptoticsBreakdown PointBayesian RobustnessAn expanded treatment of robust regression and pseudo-values is also featured, and concepts, rather than mathematical completeness, are stressed in every discussion. Selected numerical algorithms for computing robust estimates and convergence proofs are provided throughout the book, along with quantitative robustness information for a variety of estimates. A General Remarks section appears at the beginning of each chapter and provides readers with ample motivation for working with the presented methods and techniques.Robust Statistics, Second Edition is an ideal book for graduate-level courses on the topic. It also serves as a valuable reference for researchers and practitioners who wish to study the statistical research associated with robust statistics.

User’s Reviews

Editorial Reviews: Review “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 Inside Flap A new edition of the classic, groundbreaking book on robust statisticsOver twenty-five years after the publication of its predecessor, Robust Statistics, Second Edition continues to provide an authoritative and systematic treatment of the topic. This new edition has been thoroughly updated and expanded to reflect the latest advances in the field while also outlining the established theory and applications for building a solid foundation in robust statistics for both the theoretical and the applied statistician.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. In addition to an extended treatment of robust regression, the Second Edition features four new chapters covering:Robust TestsSmall Sample AsymptoticsBreakdown PointBayesian RobustnessAn expanded treatment of robust regression and pseudo-values is also featured, and concepts, rather than mathematical completeness, are stressed in every discussion. Selected numerical algorithms for computing robust estimates and convergence proofs are provided throughout the book, along with quantitative robustness information for a variety of estimates. A General Remarks section appears at the beginning of each chapter and provides readers with ample motivation for working with the presented methods and techniques.Robust Statistics, Second Edition is an ideal book for graduate-level courses on the topic. It also serves as a valuable reference for researchers and practitioners who wish to study the statistical research associated with robust statistics. From the Back Cover A new edition of the classic, groundbreaking book on robust statisticsOver twenty-five years after the publication of its predecessor, Robust Statistics, Second Edition continues to provide an authoritative and systematic treatment of the topic. This new edition has been thoroughly updated and expanded to reflect the latest advances in the field while also outlining the established theory and applications for building a solid foundation in robust statistics for both the theoretical and the applied statistician.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. In addition to an extended treatment of robust regression, the Second Edition features four new chapters covering:Robust TestsSmall Sample AsymptoticsBreakdown PointBayesian RobustnessAn expanded treatment of robust regression and pseudo-values is also featured, and concepts, rather than mathematical completeness, are stressed in every discussion. Selected numerical algorithms for computing robust estimates and convergence proofs are provided throughout the book, along with quantitative robustness information for a variety of estimates. A General Remarks section appears at the beginning of each chapter and provides readers with ample motivation for working with the presented methods and techniques.Robust Statistics, Second Edition is an ideal book for graduate-level courses on the topic. It also serves as a valuable reference for researchers and practitioners who wish to study the statistical research associated with robust statistics. About the Author Peter J. Huber, PhD, has over thirty-five years of academic experience and has previously served as professor of statistics at ETH Zurich (Switzerland), Harvard University, Massachusetts Institute of Technology, and the University of Bayreuth (Germany). An established authority in the field of robust statistics, Dr. Huber is the author or coauthor of four books and more than seventy journal articles in the areas of statistics and data analysis. Elvezio M. Ronchetti, PhD, is Professor of Statistics in the Department of Econometrics at the University of Geneva in Switzerland. Dr. Ronchetti is a Fellow of the American Statistical Association and coauthor of Robust Statistics: The Approach Based on Influence Functions, also published by Wiley. Read more

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐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!

⭐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.

⭐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.

⭐Excellent livre,que je recommande vivement à tous les passionnés de statistiques.Livre bien reçu rapidement dans un emballage soigné, totale satisfaction.

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