Kernel Smoothing Regression

Version (2.16 KB) by Yi Cao
A non-parametrical regression (smoothing) tool using Gaussian kernel.


Updated 24 Dec 2008

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Non-parametric regression is widely used in many scientific and engineering areas, such as image processing and pattern recognition.

Non-parametric regression is about to estimate the conditional expectation of a random variable:

E(Y|X) = f(X)

where f is a non-parametric function.

Based on the kernel density estimation technique, this code implements the so called Nadaraya-Watson kernel regression algorithm particularly using the Gaussian kernel. The default bandwidth of the regression is derived from the optimal bendwidth of the Gaussian kernel density estimation suggested in the literature. The code can also take care of missing data.

Cite As

Yi Cao (2023). Kernel Smoothing Regression (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2007b
Compatible with any release
Platform Compatibility
Windows macOS Linux

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Version Published Release Notes

add an error case

update with error checking.