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Chapter 74. The ROBUSTREG Procedure > Overview: ROBUSTREG Procedure - Pg. 5642

5642 ! Chapter 74: The ROBUSTREG Procedure Overview: ROBUSTREG Procedure The main purpose of robust regression is to detect outliers and provide resistant (stable) results in the presence of outliers. In order to achieve this stability, robust regression limits the influence of outliers. Historically, three classes of problems have been addressed with robust regression techniques: problems with outliers in the y-direction (response direction) problems with multivariate outliers in the x-space (i.e., outliers in the covariate space, which are also referred to as leverage points) problems with outliers in both the y-direction and the x-space Many methods have been developed in response to these problems. However, in statistical applica- tions of outlier detection and robust regression, the methods most commonly used today are Huber M estimation, high breakdown value estimation, and combinations of these two methods. The RO- BUSTREG procedure in SAS 9.2 provides four such methods: M estimation, LTS estimation, S estimation, and MM estimation.