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Robust Statistics

ISBN: 978-0-471-72524-4
320 pages
February 2005
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Description

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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Table of Contents

1. Generalities.

2. The Weak Topology and Its Metrization.

3. The Basic Types of Estimates.

4. Asymptotic Minimax Theory for Estimating a Location Parameter.

5. Scale Estimates.

6. Multiparameter Problems, In Particular Joint Estimation of Location and Scale.

7. Regression.

8. Robust Covariance and Correlation Matrices.

9. Rubustness of Design.

10. Exact Finite Sample Results.

11. Miscellaneous Topics.

References. 

Index.

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Author Information

Peter J. Huber was formerly a Professor of Statistics at Harvard University and ETH Zurich. Dr. Huber received his Ph.D. in Mathematics from ETH Zurich in 1961.
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Reviews

“…informative and worth the effort for anyone with an interest in this field...” (ISCB News 38, December 2004)
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