The SAS/STAT distribution analysis procedures include the following:
 KDE Procedure — Performs univariate and bivariate kernel density estimation
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.
The following are highlights of the KDE procedure's features:
 computes a variety of common statistics, including estimates of the percentiles of the hypothesized probability density function
 produces a variety of plots, including univariate and bivariate histograms, plots of the kernel density estimates, and contour plots
 saves kernel density estimates into SAS data sets

 performs BY group processing, which enables you to obtain separate analyses on grouped observations
 perform weighted estimation
 create a SAS data set that corresponds to any output table
 automatically creates graphs by using ODS Graphics

For further details, see
KDE Procedure