Robust Statistical Procedures 2/e

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.