The LOESS Procedure
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Overview
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Getting Started
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Syntax
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Details
Missing Values Output Data Sets Data Scaling Direct versus Interpolated Fitting kd Trees and Blending Local Weighting Iterative Reweighting Specifying the Local Polynomials Smoothing Matrix Model Degrees of Freedom Statistical Inference and Lookup Degrees of Freedom Automatic Smoothing Parameter Selection Sparse and Approximate Degrees of Freedom Computation Scoring Data Sets ODS Table Names ODS Graphics
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Examples
- References
| Smoothing Matrix |
When no iterative reweighting is done, the "Smoothing Matrix" denoted by 
defines the linear relationship between the fitted and observed dependent variable values of a loess model. You can obtain the predicted values of a loess fit from the observed values via
 |
where 
is the vector of observed values and 
is the corresponding vector of predicted values of the dependent variable. Note that is an 
by matrix, where is the number of observations in the analysis. PROC LOESS does not explicitly form if the DFMETHOD=EXACT option is not explicitly or implicitly selected.