SAS/IML Robust Regression Examples
Robust RegressionSAS/IML software now provides subroutines that can be used for outlier detection and robust regression. The Least Median of Squares (LMS) and Least Trimmed Squares (LTS) subroutines perform robust regression (sometimes called resistant regression). These subroutines are able to detect outliers and perform a least-squares regression on the remaining observations. The Minimum Volume Ellipsoid Estimation (MVE) subroutine can be used to find the minimum volume ellipsoid estimator, which is the location and ro bust covariance matrix that can be used for constructing confidence regions and for detecting multivariate outliers and leverage points. Moreover, the MVE subroutine provides a table of robust distances and classical Mahalanobis distances. The LMS, LTS, a nd MVE subroutines and some other robust estimation theories and methods were developed by Rousseeuw (1984) and Rousseeuw and Leroy (1987). Some statistical applications for MVE are described in Rousseeuw and Van Zomeren (1990).
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