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Robust Regression With SAS/IML Software

Four SAS/IML subroutines 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 can detect outliers and perform a least squares regression on the remaining observations. The minimum volume ellipsoid estimation (MVE) and minimum covariance determinant estimation (MCD) subroutines can be used to find a robust location and a robust covariance matrix, respectively, that can be used for constructing confidence regions, detecting multivariate outliers and leverage points, and conducting robust canonical correlation and principal component analysis.

Examples

Using LMS and LTS
Online Documentation Examples

Using MVE and MCD
Online Documentation Examples