Overview: KDE Procedure

The KDE procedure performs univariate and bivariate kernel density estimation. Statistical density estimation involves approximating a hypothesized probability density function from observed data. Kernel density estimation is a nonparametric technique for density estimation in which a known density function (the kernel) is averaged across the observed data points to create a smooth approximation. PROC KDE uses a Gaussian density as the kernel, and its assumed variance determines the smoothness of the resulting estimate. Refer to Silverman (1986) for a thorough review and discussion.

You can use PROC KDE to compute a variety of common statistics, including estimates of the percentiles of the hypothesized probability density function. You can produce a variety of plots, including univariate and bivariate histograms, plots of the kernel density estimates, and contour plots. You can also save kernel density estimates into SAS data sets.