nprobust 0.5.0

Nonparametric Robust Estimation and Inference Methods

nprobust

Python implementation of the R package nprobust: Nonparametric Robust Estimation and Inference Methods using Local Polynomial Regression and Kernel Density Estimation.

Description

This package provides tools for data-driven statistical analysis using local polynomial regression and kernel density estimation methods as described in:

• Calonico, Cattaneo and Farrell (2018): "On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference", Journal of the American Statistical Association.
• Calonico, Cattaneo and Farrell (2019): "nprobust: Nonparametric Kernel-Based Estimation and Robust Bias-Corrected Inference", Journal of Statistical Software.

Installation

pip install nprobust .

Or with plotting support:

Main Functions

• lprobust: Local polynomial point estimation and robust bias-corrected inference
• lpbwselect: Bandwidth selection for local polynomial regression
• kdrobust: Kernel density point estimation and robust bias-corrected inference
• kdbwselect: Bandwidth selection for kernel density estimation
• nprobust_plot: Plotting function for estimation results

Basic Usage

import numpy as np
from nprobust import lprobust, lpbwselect, kdrobust, kdbwselect, nprobust_plot

# Generate sample data
np.random.seed(42)
n = 500
x = np.random.uniform(0, 1, n)
y = np.sin(2 * np.pi * x) + np.random.normal(0, 0.5, n)

# Local polynomial regression
result = lprobust(y, x)
result.summary()

# Bandwidth selection
bw = lpbwselect(y, x, bwselect="mse-dpi")
bw.summary()

# Kernel density estimation
kd_result = kdrobust(x)
kd_result.summary()

# Plotting
fig = nprobust_plot(result, title="Local Polynomial Regression")

Parameters

lprobust

• y: Response variable
• x: Independent variable
• eval: Evaluation points (default: 30 equally spaced points)
• p: Polynomial order (default: 1)
• deriv: Order of derivative (default: 0)
• h: Bandwidth (default: data-driven selection)
• kernel: Kernel function ('epa', 'uni', 'tri', 'gau')
• bwselect: Bandwidth selection method ('mse-dpi', 'imse-dpi', etc.)
• vce: Variance estimator ('nn', 'hc0', 'hc1', 'hc2', 'hc3')

kdrobust

• x: Data vector
• eval: Evaluation points
• h: Bandwidth
• kernel: Kernel function ('epa', 'uni', 'gau')
• bwselect: Bandwidth selection method

References

• Calonico, S., M. D. Cattaneo, and M. H. Farrell (2018). "On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference." Journal of the American Statistical Association 113(522): 767-779.
• Calonico, S., M. D. Cattaneo, and M. H. Farrell (2019). "nprobust: Nonparametric Kernel-Based Estimation and Robust Bias-Corrected Inference." Journal of Statistical Software 91(8): 1-33.

License

GPL-2

Original R Package

https://github.com/nppackages/nprobust