SAS/STAT Software


The ROBUSTREG procedure provides resistant (stable) results for linear regression models in the presence of outliers. The following are highlights of the ROBUSTREG procedure's features:

  • provides 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
  • performs BY group processing, which enables you to obtain separate analyses on grouped observations
  • perform weighted estimation
  • creates a SAS data set that contains the parameter estimates and the estimated covariance matrix
  • creates an output SAS data set that contains statistics that are calculated after fitting the model
  • creates a SAS data set that corresponds to any output table
  • automatically creates fit plots and diagnostic plots by using ODS Graphics

For further details see the ROBUSTREG Procedure