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.
Yi Cao (2021). Kernel Smoothing Regression (https://www.mathworks.com/matlabcentral/fileexchange/19195-kernel-smoothing-regression), MATLAB Central File Exchange. Retrieved .
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Inspired by: Update PDF Estimation
Inspired: Multivariant Kernel Regression and Smoothing, Local Linear Kernel Regression, Volatility Surface, Kernel Regression with Variable Window Width, 3D Plot for Greeks, Plot Some Paths, Coin And Dice, Brain Teaser Solver, Foreign Exchange Options, Log-Uniform Jump-Diffusion Model, Ogive optimization toolbox
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