Identification and use of nonparametric noise models extracted from overlapping subrecords

Models are very important for studying, understanding, controlling, predicting and optimizing observed phenomena. Since, these models should grasp the observed reality as good as possible, they should be based on real life data. Unfortunately life is not ideal and measurements are prone to errors. These errors can completely blur our view of the observed phenomena. To optimally reduce the influence of these measurement errors on the model one needs to quantify the behavior and nature of these errors, this is done with nonparametric noise models. To develop these noise models, we often need long and costly experiments. The classical theory requires at least six repetitions of the same experiments to ensure the good quality of the noise models. Besides that, the sub experiments should be long enough to obtain a good match between model and observations. The main contribution of this thesis is to obtain good quality noise models with only 2 repetitions. Recycling is very important nowadays, even in the modeling world! So the strategy used in this thesis allows one to recycle measurements points from both repetitions. This results in overlapping subrecords. This recycling strategy works remarkably well and its performance is studied in full detail.