Online Univariate Outlier Detection in Final Test: A Robust Rolling Horizon Approach

H.C.M. Bossers; Johann L. Hurink; Gerardus Johannes Maria Smit

doi:10.1109/ETS.2011.10

Online Univariate Outlier Detection in Final Test: A Robust Rolling Horizon Approach

H.C.M. Bossers, Johann L. Hurink, Gerardus Johannes Maria Smit

Discrete Mathematics and Mathematical Programming

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

2 Citations (Scopus)

Abstract

We present an online outlier detection method that is applicable to Final Test. Test limits are constructed based on previous measurements and robust statistics are used to ensure a stable start to the method. We analyze our method using real-world data. Furthermore, we identified some cases which can result in performance degradation, but most experiments show that our method is robust to outliers and able to detect them in an online setting.

Original language	Undefined
Title of host publication	16th IEEE European Test Symposium, ETS 2011
Publisher	IEEE Computer Society
Pages	201-201
Number of pages	1
ISBN (Print)	978-1-4577-0483-3
DOIs	https://doi.org/10.1109/ETS.2011.10
Publication status	Published - 23 May 2011
Event	16th IEEE European Test Symposium, ETS 2011 - Trondheim, Norway Duration: 23 May 2011 → 27 May 2011 Conference number: 16

Publication series

Name
Publisher	IEEE Computer Society
ISSN (Print)	1530-1877

Conference

Conference	16th IEEE European Test Symposium, ETS 2011
Abbreviated title	ETS
Country	Norway
City	Trondheim
Period	23/05/11 → 27/05/11

Keywords

IR-77801
EWI-20372
METIS-277735

Access to Document

10.1109/ETS.2011.10

http://eprints.eemcs.utwente.nl/secure2/20372/01/05957947.pdf

Cite this

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abstract = "We present an online outlier detection method that is applicable to Final Test. Test limits are constructed based on previous measurements and robust statistics are used to ensure a stable start to the method. We analyze our method using real-world data. Furthermore, we identified some cases which can result in performance degradation, but most experiments show that our method is robust to outliers and able to detect them in an online setting.",

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KW - METIS-277735

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