Outlier detection in multivariate calibration

A new procedure for the identification of multiple outliers in linear models is proposed. It is based on an evolution program for subset selection. The procedure is illustrated and compared to existing robust methods, using several data sets known to contain multiple outliers and its performance and robustness are additionally tested by a Monte Carlo study on the simulated data sets.