ROBUSTREG Procedure


9.1  

The ROBUSTREG procedure provides resistant (stable) results in the presence of outliers by limiting the influence of outliers. In statistical applications of outlier detection and robust regression, the methods most commonly used today are Huber (1973) M estimation, high breakdown value estimation, and combinations of these two methods. The ROBUSTREG procedure provides four such methods: M estimation, LTS estimation, S estimation, and MM estimation. With these four methods , the ROBUSTREG procedure acts as an integrated tool for outlier detection and robust regression with various contaminated data. The ROBUSTREG procedure is scalable such that it can be used for applications in data cleansing and data mining.




SAS.STAT 9.1 Users Guide (Vol. 6)
SAS.STAT 9.1 Users Guide (Vol. 6)
ISBN: N/A
EAN: N/A
Year: 2004
Pages: 127

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