Robust Statistics Toolkit

Purpose

Provides robust statistical methods resistant to outliers and model violations for reliable inference.

Capabilities

• M-estimators (Huber, Tukey)
• Trimmed and winsorized estimators
• Robust regression (MM-estimation)
• Breakdown point analysis
• Influence function computation
• Robust covariance estimation

Usage Guidelines

1. Outlier Detection: Identify potential outliers first
2. Estimator Selection: Choose based on expected contamination
3. Breakdown Point: Consider required breakdown point
4. Efficiency: Balance robustness and efficiency

Tools/Libraries

• robustbase (R)
• scikit-learn
• statsmodels