Introduction to Robust and Quasi-Robust Statistical Methods.

Introduction and Summary.- 1.1. History and main contributions.- 1.2. Why robust estimations?.- 1.3. Summary.- A The Theoretical Background.- 2. Sample spaces, distributions, estimators.- 2.1. Introduction.- 2.2. Example.- 2.3. Metrics for probability distributions.- 2.4. Estimators seen as functionals of distributions.- 3. Robustness, breakdown point and influence function.- 3.1. Definition of robustness.- 3.2. Definition of breakdown point.- 3.3. The influence function.- 4. The jackknife method.- 4.1. Introduction.- 4.2. The jackknife advanced theory.- 4.3. Case study.- 4.4. Comments.- 5. Bootstrap methods, sampling distributions.- 5.1. Bootstrap methods.- 5.2. Sampling distribution of estimators.- B.- 6. Type M estimators.- 6.1. Definition.- 6.2. Influence function and variance.- 6.3. Robust M estimators.- 6.4. Robustness, quasi-robustness and non-robustness.- 6.4.1. Statement of the location problem.- 6.4.2. Least powers.- 6.4.3. Huber's function.- 6.4.4. Modification to Huber's proposal.- 6.4.5. Function Fair.- 6.4.6. Cauchy-s function.- 6.4.7. Welsch-s function.- 6.4.8. Bisquare function.- 6.4.9. Andrews's function.- 6.4.10. Selection of the ?-function.- 7. Type L estimators.- 7.1. Definition.- 7.2. Influence function and variance.- 7.3. The median and related estimators.- 8. Type R estimator.- 8.1. Definition.- 8.2. Influence function and variance.- 9. Type MM estimators.- 9.1. Definition.- 9.2. Influence function and variance.- 9.3. Linear model and robustness - Generalities.- 9.4. Scale of residuals.- 9.5. Robust linear regression.- 9.6. Robust estimation of multivariate location and scatter.- 9.7. Robust non-linear regression.- 9.8. Numerical methods.- 9.8.1. Relaxation methods.- 9.8.2. Simultaneous solutions.- 9.8.3 Solution of fixed-point and non-linear equations.- 10. Quantile estimators and confidence intervals.- 10.1. Quantile estimators.- 10.2. Confidence intervals.- 11. Miscellaneous.- 11.1. Outliers and their treatment.- 11.2. Analysis of variance, constraints on minimization.- 11.3. Adaptive estimators.- 11.4. Recursive estimators.- 11.5. Concluding remark.- 12. References.- 13. Subject index.