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. The following are highlights of the KDE procedure's features:


For further details see the SAS/STAT User's Guide:
The KDE Procedure
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