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Woodslane Online Catalogues

9780898713794 Academic Inspection Copy

Robust Statistical Procedures

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Here is a brief, and easy-to-follow introduction and overview of robust statistics. Peter Huber focuses primarily on the important and clearly understood case of distribution robustness, where the shape of the true underlying distribution deviates slightly from the assumed model (usually the Gaussian law). An additional chapter on recent developments in robustness has been added and the reference list has been expanded and updated from the 1977 edition.
Preface to the Second Edition Preface to the First Edition Chapter 1: Background. Why robust procedures? Chapter 2: Qualitative and Quantitative Robustness. Qualitative robustness Quantitative robustness, breakdown Infinitesimal robustness, influence function Chapter 3: M-,L-, and R-Estimates. M-estimates L-estimates R-estimates Asymptotic properties of M-estimates Asymptotically efficient M-, L-, R-estimates Scaling question Chapter 4: Asymptotic Minimax Theory. Minimax asymptotic bias Minimax asymptotic variance Chapter 5: Multiparameter Problems. Generalities Regression Robust covariances: the affinely invariant case Robust covariances: the coordinate dependent case Chapter 6: Finite Sample Minimax Theory. Robust tests and capacities Finite sample minimax estimation Chapter 7: Adaptive Estimates. Adaptive estimates Chapter 8: Robustness: Where are We Now? The first ten years Influence functions and psuedovalues Breakdown and outlier detection Studentizing Shrinking neighborhoods Design Regression Multivariate problems Some persistent misunderstandings Future directions References.
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