| Abstract: | Abstract In every mathematical (e.g., statistical) procedure and theorem used in calibration, several conditions need to be fulfilled. What can analysts and chemometricians do, however, if the conditions are only nearly fulfilled? One can expect that small changes in the conditions yield only small changes in the results. This article shows how to treat two types of model error caused by assuming an incorrect error distribution or relationship (i.e., linear). The procedures applied are based on robust statistics and fuzzy theory, respectively. |
