Week 1: Introduction to Robustness with examples
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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
- Chapters 16
- Department Statistics
- Teacher
Dr. Hafiz Zafar Nazir
