Detecting Outliers Using Medcouple – A Simple, Robust Approach

Python Implementation along with Required Files:

https://drive.google.com/drive/folders/1i6ZN3noeTN9MCDk8fqbA1RndzV1L49dh?usp=drive_link

Risk modeling—often means dealing with skewed distributions. Standard methods like Z-scores or basic IQR can fail, either missing real outliers or flagging valid extreme values.

To address this, I used a Medcouple-based method, which is a robust, skewness-aware outlier detection technique.

How It Works

1. Compute Ratios – Transform raw variables into a ratio (e.g., X2 / X1).

2. Center Around Median – Scale values relative to the median to preserve asymmetry.

3. Estimate Spread Robustly – Use quartiles above and below the median to calculate IQR.

4. Measure Skewness (Medcouple) – A robust statistic capturing asymmetry without being influenced by extremes.

5. Adjust Outlier Bounds – Expand or shrink thresholds based on skewness for accurate detection.

6. Identify Outliers – Flag observations outside the skewness-adjusted bounds.

Benefits

• Handles skewed and heavy-tailed distributions

• Preserves meaningful extreme values

• Improves data quality for modeling and analysis

Attached Files

To make this reproducible, I’m sharing:

1. Excel replication – See the method step by step in Excel

2. Python implementation – Fully automated outlier detection

3. Input data used in Python – The dataset for replication