Asymptotic theory of outlier detection algorithms for linear time series regression models - Johansen, S; Nielsen, B (Wiley, 2015)
Outlier detection algorithms are intimately connected with robust statistics that down-weight some observations to zero. We deÖne a number of outlier detection algorithms related to the Huber-skip and the Least Trimmed Squares estimators, including the 1-step Huber skip estimator and the Forward Search. Next, we review a recently developed asymptotic theory of these. Finally, we analyze the gauge, the fraction of wrongly detected outliers, for a number of outlier detection algorithms and establish an asymptotic normal and a Poisson theory for the gauge.