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- SAS/STAT Procedures A-Z
The ROBUSTREG Procedure
- provides resistant (stable) results for linear regression models in the presence of outliers
- offers four estimation methods: M, LTS, S, and MM
- provides 10 weight functions for M estimation
- provides robust R2 and deviance for all estimates
- provides asymptotic covariance and confidence intervals for regression parameter with the M, S, and MM methods
- provides robust Wald and F tests for regression parameters with the M and MM methods
- provides outlier and leverage-point diagnostics
- supports parallel computing for S and LTS estimates
- produces fit plots and diagnostic plots by using ODS Graphics
- obtain separate analyses on observations in groups
- perform weighted estimation
- creates a SAS data set containing the parameter estimates and the estimated covariance matrix
- creates an output SAS data set containing statistics calculated after fitting the model
- uses ODS to create a SAS data set corresponding to any table
For further details see the SAS/STAT User's Guide:
The ROBUSTREG Procedure
( PDF | HTML )
Examples
- Comparison of Robust Estimates
- Robust ANOVA
- Growth Study of De Long and Summers
- Constructed Effects
- Robust Diagnostics
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