Robust statistical methods resistant to outliers
name robust-statistics-toolkit description Robust statistical methods resistant to outliers allowed-tools ["Bash","Read","Write","Edit","Glob","Grep"] metadata {"specialization":"mathematics","domain":"science","category":"statistical-computing","phase":6} 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 Outlier Detection : Identify potential outliers first Estimator Selection : Choose based on expected contamination Breakdown Point : Consider required breakdown point Efficiency : Balance robustness and efficiency Tools/Libraries robustbase (R) scikit-learn statsmodels