Nonparametric Kernel Smoother
A kernel estimator uses an explicitly defined set of
weights at each point x to produce the estimate at x.
The kernel estimator of f has the form
where W is the weight
function that depends on the smoothing parameter .
The weights are derived from a single
function that is independent of the design
where K0 is a kernel function and
is the bandwidth or smoothing parameter.
The weights are nonnegative and sum to 1.
Symmetric probability density functions
commonly used as kernel functions are
You select a bandwidth for each kernel
estimator by specifying c in the formula
where Q is the sample interquartile range of the
explanatory variable and n is the sample size.
This formulation makes c independent of the units of X.
SAS/INSIGHT software divides the range of the explanatory
variable into 128 evenly spaced intervals, then approximates
the data on this grid and uses the fast Fourier transformation
(Silverman 1986) to estimate the kernel fit on this grid.
For a data point xi that lies
between two grid points, a linear interpolation
is used to compute the predicted value.
A small value of (relative to the width of the
interval) may give unstable estimates of the kernel fit.
After choosing Curves:Kernel, you specify a kernel and
smoothing parameter selection method in the Kernel Fit dialog.
Figure 39.42: Kernel Fit Dialog
The default Weight:Normal uses a normal weight,
and Method:GCV uses a c value that minimizes
.Figure 39.43 illustrates normal
kernel estimates with c values of
0.0944 (the GCV value) and 0.7546 (DF=3).
Use the slider to change the c value of the kernel fit.
Figure 39.43: Kernel Estimates
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.