The LOESS Procedure
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Overview
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Getting Started
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Syntax
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DetailsMissing ValuesOutput Data SetsData ScalingDirect versus Interpolated Fittingk-d Trees and BlendingLocal WeightingIterative ReweightingSpecifying the Local PolynomialsSmoothing MatrixModel Degrees of FreedomStatistical Inference and Lookup Degrees of FreedomAutomatic Smoothing Parameter SelectionSparse and Approximate Degrees of Freedom ComputationScoring Data SetsODS Table NamesODS Graphics
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Examples
- References
The LOESS procedure implements a nonparametric method for estimating regression surfaces pioneered by Cleveland, Devlin, and Grosse (1988); Cleveland and Grosse (1991); 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:
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fits nonparametric models
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supports the use of multidimensional data
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supports multiple dependent variables
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supports both direct and interpolated fitting that uses k-d trees
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performs statistical inference
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performs automatic smoothing parameter selection
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performs iterative reweighting to provide robust fitting when there are outliers in the data
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produces graphs with ODS Graphics