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Asymptotic Theory of Outlier Detection Algorithms for Linear Time Series Regression Models
Authors:Søren Johansen  Bent Nielsen
Affiliation:1. Department of EconomicsUniversity of Copenhagen and CREATES, Aarhus University;2. Nuffield College and Department of EconomicsUniversity of Oxford
Abstract:Outlier detection algorithms are intimately connected with robust statistics that down‐weight some observations to zero. We define a number of outlier detection algorithms related to the Huber‐skip and least trimmed squares estimators, including the one‐step Huber‐skip estimator and the forward search. Next, we review a recently developed asymptotic theory of these. Finally, we analyse 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.
Keywords:forward search   gauge   Huber‐skip   impulse indicator saturation   iterated martingale inequality   iteration of one‐step estimators   one‐step Huber‐skip   robustified least squares   weighted and marked empirical processes
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