Robust orthogonal regression for the outlier detection when comparing two series of measurement results

The application of a robust orthogonal regression procedure is described for the outlier detection during the evaluation of a method comparison study and, in general, when comparing two series of measurement results. The procedure is based on the least median of the squared orthogonal residuals (orthogonal LMS). It is concluded that the orthogonal LMS procedure is a useful screening tool to detect outlying values in a data set. Especially for data sets with extreme values it is recommended to combine the robust data evaluation with a visual display. For the comparison of heteroscedastic methods, the program can easily be adapted to a weighted orthogonal LMS variant.