Robust Shift Detection in Autoregressive Processes

Abstract

We use a local approach and highly robust estimators for the detection of level shifts in autocorrelated data. The arising detection rules identify shifts with a short delay, resist some outliers and adapt to time-varying model parameters. The performance of the rules is investigated using analytical resistances and via simulations.