Robust Statistics / Edition 1

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Overview

Robust Statistics, Second Edition includes four new chapters on the following topics: robust tests; small sample asymptotics; breakdown point; and Bayesian robustness. A new section on time series has also been included. The first edition of this book was the first systematic, book-length treatment of robust statistics. The book begins with a general introduction and the formal mathematical background behind qualitative and quantitative robustness. A solid foundation of robust statistics for both the theoretical and the applied statistician is provided. The book successfully reorganizes, summarizes, and extends information that has been available in part thus far. Concepts are stressed throughout rather than mathematical completeness, and selected numerical algorithms for computing robust estimates, as well as convergence proofs, are provided. Quantitative robustness information for a variety of estimates is contained within tables throughout.
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Editorial Reviews

From the Publisher
"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)
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Product Details

Meet 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.

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Table of Contents

1 Generalities

2 The Weak Topology and its Metrization

3 The Basic Types of Estimates

4 Asymptotic Minimax Theory for Estimating Location

5 Scale Estimates

6 Multiparameter Problems, in Particular Joint Estimation of Location and Scale

7 Regression

8 Robust Covariance and Correlation Matrices

9 Robustness of Design

10 Exact Finite Sample Results

11 Finite Sample Breakdown Point

12 Infinitesimal Robustness

13 Robust Tests

14 Small Sample Asymptotics

15 Bayesian Robustness

References

Index

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