epa_smoothing

EPA Kernel-Weighted Local Median Smoothing - Robust nonparametric curve smoothing

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Information

Property	Value
Language	HTML
Stars	0
Forks	0
Watchers	0
Open Issues	0
License	No License
Created	2026-01-15
Last Updated	2026-03-25
Last Push	2026-01-21
Contributors	1
Default Branch	main
Visibility	private

Reproducibility

This repository includes reproducibility tools:

• Python requirements.txt

Status

• Issues: Enabled
• Wiki: Enabled
• Pages: Enabled

README

EPA Kernel-Weighted Local Median Smoothing

Robust nonparametric curve smoothing using the Epanechnikov kernel and weighted median.

Live Demo

Try the Interactive Dashboard

Features

• Robust to Outliers: Weighted median ignores outlier magnitude, achieving up to 40% lower error than mean-based smoothers
• Optimal Kernel: Epanechnikov kernel minimizes AMISE among second-order kernels
• Interactive Visualization: Real-time parameter adjustment with animations

Quick Start

import numpy as np
from epa_smoothing import epa_local_median

# Noisy data with outliers
x = np.linspace(0, 2*np.pi, 100)
y = np.sin(x) + np.random.normal(0, 0.3, 100)

# Smooth
y_smooth = epa_local_median(x, y, bandwidth=0.5)

Documentation

• Theory - Mathematical background
• API - Function documentation
• Examples - Usage examples
• Code - Full source code

Key Results

Metric	EPA Median	EPA Mean	Improvement
RMSE (8% outliers)	0.108	0.184	41%
Breakdown Point	50%	0%	Infinitely more robust

License

MIT