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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.

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