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Course Material

Week 1: Introduction to Robustness with examples
Week 2: Objective functions for optimization and deriving the robust estimators
Week 3: M-estimator of location and M-estimator Scale along with their variances comparisons
Week 4: E-estimator of location and Scale with application in R language
Week 5: R-estimator of location and Scale and the derivation of variance along with application on real data sets
Week 6: W-estimator of location and Scale
Week 7: Theoretical comparisons of usual and Robust estimators
Week 8: Simulation of estimators under normal and non-normal environments for comparative point of view
Week 9: Sensitivity curve and Influence function of location
Week 10: Sensitivity curve and Influence function of Scale
Week 11: Standardized variances of robust estimators
Week 12: Robustness under contaminated environments
Week 13: Walsh averages and their uses and Walsh averages based estimators
Week 14: Outliers and influential observations
Week 15: The Breakdown point of Robust estimators
Week 16: Outliers in Regression analysis and Robust regression

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