SAS/STAT Software

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 the SAS/STAT User's Guide: The KDE Procedure