Chapter 41: The LOESS Procedure

Overview

The LOESS procedure implements a nonparametric method for estimating regression surfaces pioneered by Cleveland, Devlin, and Grosse (1988), Cleveland and Grosse (1991), and Cleveland, Grosse, and Shyu (1992). the LOESS procedure allows great flexibility because no assumptions about the parametric form of the regression surface are needed.

The SAS System provides many regression procedures such as the GLM, REG, and NLIN procedures for situations in which you can specify a reasonable parametric model for the regression surface. You can use the LOESS procedure for situations in which you do not know a suitable parametric form of the regression surface. Furthermore, the LOESS procedure is suitable when there are outliers in the data and a robust fitting method is necessary.

The main features of the LOESS procedure are as follows :

• fits nonparametric models

• supports the use of multidimensional data

• supports multiple dependent variables

• supports both direct and interpolated fitting using kd trees

• performs statistical inference

• performs automatic smoothing parameter selection

• performs iterative reweighting to provide robust fitting when there are outliers in the data

• supports multiple SCORE statements

Experimental graphics are now available with the LOESS procedure. For more information, refer to the ODS Graphics section on page 2248.

Local Regression and the Loess Method

Assume that for i = 1 to n , the i th measurement y _i of the response y and the corresponding measurement x _i of the vector x of p predictors are related by

where g is the regression function and ˆˆ _i is a random error. The idea of local regression is that at a predictor x , the regression function g ( x ) can be locally approximated by the value of a function in some specified parametric class. Such a local approximation is obtained by fitting a regression surface to the data points within a chosen neighborhood of the point x .

In the loess method, weighted least squares is used to fit linear or quadratic functions of the predictors at the centers of neighborhoods. The radius of each neighborhood is chosen so that the neighborhood contains a specified percentage of the data points. The fraction of the data, called the smoothing parameter, in each local neighborhood controls the smoothness of the estimated surface. Data points in a given local neighborhood are weighted by a smooth decreasing function of their distance from the center of the neighborhood.

In a direct implementation, such fitting is done at each point at which the regression surface is to be estimated. A much faster computational procedure is to perform such local fitting at a selected sample of points in predictor space and then to blend these local polynomials to obtain a regression surface.

You can use the LOESS procedure to perform statistical inference provided the error distribution satisfies some basic assumptions. In particular, such analysis is appropriate when the ˆˆ _i are i.i.d. normal random variables with mean 0. By using the iterative reweighting, the LOESS procedure can also provide statistical inference when the error distribution is symmetric but not necessarily normal. Furthermore, by doing iterative reweighting, you can use the LOESS procedure to perform robust fitting in the presence of outliers in the data.

While all output of the LOESS procedure can be optionally displayed, most often the LOESS procedure is used to produce output data sets that will be viewed and manipulated by other SAS procedures. PROC LOESS uses the Output Delivery System (ODS) to place results in output data sets. This is a departure from older SAS procedures that provide OUTPUT statements to create SAS data sets from analysis results.