Asymptotic Theory of Outlier Detection Algorithms for Linear Time Series Regression Models

Søren Johansen; Bent Nielsen

doi:10.1111/sjos.12174

Asymptotic Theory of Outlier Detection Algorithms for Linear Time Series Regression Models

31 Citationer (Scopus)

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.

Originalsprog	Engelsk
Tidsskrift	Scandinavian Journal of Statistics
Vol/bind	43
Udgave nummer	2
Sider (fra-til)	321-348
Antal sider	28
ISSN	0303-6898
DOI	https://doi.org/10.1111/sjos.12174
Status	Udgivet - 1 jun. 2016

Adgang til dokumentet

10.1111/sjos.12174

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/sjos.12174

Citationsformater

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AB - 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.

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