Chapter 36: The KDE Procedure

Overview

The KDE procedure performs either univariate or 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 . Refer to Silverman (1986) for a thorough review and discussion.

PROC KDE uses a Gaussian density as the kernel, and its assumed variance determines the smoothness of the resulting estimate. PROC KDE outputs the kernel density estimate into a SAS data set, which you can then use with other procedures for plotting or analysis. PROC KDE also computes a variety of common statistics, including estimates of the percentiles of the hypothesized probability density function.

Experimental graphics are now available with the KDE procedure. For more information, see the ODS Graphics section on page 2009.