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Robust regression and outlier detection Annick M. Leroy, Peter J. Rousseeuw
Publisher: Wiley

I've conducted a lot of univariate analyses in SAS, yet I'm always surprised when the best way to carry out the analysis uses a SAS regression procedure. The least squared regression with the lowest meadian squared error is chosen as the final model. Here is a plot of post 96 observations . I encountered a wonderful survey article, "Robust statistics for outlier detection," by Peter Rousseeuw and Mia Hubert. Jeuken J, Sijben A, Alenda C, Rijntjes J, Dekkers M, Boots-Sprenger S, McLendon R, Wesseling P: Robust detection of EGFR copy number changes and EGFR variant III: Technical aspects and relevance for glioma diagnostics. I always think, "This is a univariate analysis! Even if you exclude CCSM4 as an outlier the contribution to the trend since '96 from volcanic recovery in the model ensemble is about the same as the observed trend. One way is to call the ROBUSTREG procedure! The volcanic term is critical here – at the moment it looks to me as though the ENSO term is robust, and the solar fairly robust (and small) once a response function is included. Outlier identification was performed with regression analysis to detect data points at or beyond 95% confidence intervals for residuals. The ROBUSTREG procedure provides four different How can you detect univariate outliers in SAS? An adjusted boxplot rule, based on the upper and lower quartiles, along with a robust skewness estimator called the medcouple. Robust regression and outlier detection. Regression analysis identified outliers. The basis of the algorithm is Peter J. Why am I using However, you can also use the ROBUSTREG procedure to estimate robust statistics.