- Kernel (statistics)
A kernel is a weighting function used in
non-parametric estimation techniques. Kernels are used inkernel density estimation to estimaterandom variable s'density function s, or inkernel regression to estimate theconditional expectation of a random variable. Kernels are also used intime-series , in the use of theperiodogram to estimate thespectral density . An additional use is in the estimation of a time-varying intensity for apoint process .Commonly, kernel widths must also be specified when running a non-parametric estimation.
Definition
A kernel is a
non-negative real-valued integrable function "K" satisfying the following two requirements:
*int_{-infty}^{+infty}K(u)du = 1,;
*K(-u) = K(u) mbox{ for all values of } u,.The first requirement ensures that the method of kernel density estimation results in aprobability density function . The second requirement ensures that the average of the corresponding distribution is equal to that of the sample used.If "K" is a kernel, then so is the function "K"* defined by "K"*("u") = λ−1"K"(λ−1"u"), where λ > 0. This can be used to select a scale that is appropriate for the data.
Kernel functions in common use
Several types of kernels functions are commonly used: uniform, triangle, epanechnikov, quartic (biweight), tricube (triweight), gaussian, and cosine.
Below, the notation 1_{(p)},! denotes the value 1 when "p" holds, and 0 when "p" is false.
Uniform
K(u) = frac{1}{2} 1_{(|u|leq1)}
Triangle
K(u) = (1-|u|) 1_{(|u|leq1)}
Epanechnikov
K(u) = frac{3}{4}(1-u^2) 1_{(|u|leq1)}
Quartic
K(u) = frac{15}{16}(1-u^2)^2 1_{(|u|leq1)}
Triweight
K(u) = frac{35}{32}(1-u^2)^3 1_{(|u|leq1)}
Gaussian
K(u) = frac{1}{sqrt{2pie^{-frac{1}{2}u^2}
Cosine
K(u) = frac{pi}{4}cosleft(frac{pi}{2}u ight)1_{(|u|leq1)}
ee also
*
Kernel density estimation
*Kernel smoother
*Stochastic kernel
*Density estimation External links
* [http://people.revoledu.com/kardi/tutorial/Regression/KernelRegression/Kernel.htm Kernel Basis function] (with graphs).
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