# 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