Robust variance estimators in application to segmentation of measurement data distorted by impulsive and non-Gaussian noise

The paper algorithmizes the problem of regime change point identification for data measured in a system exhibiting impulsive behaviors. This is a fundamental challenge for annotation of measurement data, relevant, e.g., for designing data-driven autonomous systems. The contribution consists in formulation of an offline robust methodology based on the classical approach for structural break detection. The problem of data segmentation for the changing scale is solved as a process variable sensitive to the occurrence of a critical event in a reorganizing physical system. The advantage is that this approach does not require the existence of a variance of the data distribution. Efficiency has been evaluated for simulated data from two distributions and for real-world data sets measured in financial, mechanical, and medical systems. Simulation studies show that in the most challenging case, the error mean absolute in estimating the regime change is 20 times lower for the robust approach compared to the classical approach.