We consider outlier detection algorithms for time series regression based on iterated 1-step Huber-skip M-estimators. This paper analyses the role of varying cut-offs in such algorithms. The argument involves an asymptotic theory for a new class of weighted and marked empirical processes allowing for estimation errors of the scale and the regression coefficient.


Jiao X., Nielsen B. (2017). 'Asymptotic analysis of Iterated 1-Step Huber-Skip M-Estimators with varying cut-offs'. In: Antoch J., Jurečková J., Maciak M., Pešta M. (eds) Analytical Methods in Statistics. AMISTAT 2015. Springer Proceedings in Mathematics & Statistics, vol 193. Springer, Cham
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