A kernel smoother is a statistical technique for estimating a real valued function by using its noisy observations, when no parametric model for this function is known. The estimated function is smooth, and the level of smoothness is set by a single parameter.
This technique is most appropriate for low dimensional (p < 3) data visualization purposes. Actually, the kernel smoother represents the set of irregular data points as a smooth line or surface.
Read more about Kernel Smoother: Definitions, Nearest Neighbor Smoother, Kernel Average Smoother, Local Linear Regression, Local Polynomial Regression
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