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.
Autorentext
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.
Klappentext
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From the Reviews of Robust Statistics
"Robust Statistics is required reading for anyone involved in robustness research or desiring to start a research program in robustness."
–Journal of the American Statistical Association
Inhalt
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.