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==Kernel regression==
{{Main|Kernel regression}}
[[File:NonparRegrGaussianKernel.png|thumb| Example of a curve (red line) fit to a small data set (black points) with nonparametric regression using a Gaussian kernel smoother. The pink shaded area illustrates the kernel function applied to obtain an estimate of y for a given value of x. The kernel function defines the weight given to each data point in producing the estimate for a target point.]]{{Unreferenced section|date=August 2020}}
Kernel regression estimates the continuous dependent variable from a limited set of data points by [[Convolution|convolving]] the data points' locations with a [[kernel function]]—approximately speaking, the kernel function specifies how to "blur" the influence of the data points so that their values can be used to predict the value for nearby locations.
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