Robust Statistics Toolkit
by a5c-ai
Robust statistical methods resistant to outliers
Skill Details
Repository Files
1 file in this skill directory
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
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