How to Detect Possible Additional Outliers: Case of Interval Uncertainty

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OriginalspracheEnglisch
Seiten (von - bis)100-106
Seitenumfang7
FachzeitschriftReliable Computing
Jahrgang28
PublikationsstatusVeröffentlicht - Juni 2021

Abstract

In many practical situations, measurements are characterized by interval uncertainty - namely, based on each measurement result, the only information that we have about the actual value of the measured quantity is that this value belongs to some interval. If several such intervals - corresponding to measuring the same quantity - have an empty intersection, this means that at least one of the corresponding measurement results is an outlier, caused by a malfunction of the measuring instrument. From the purely mathematical viewpoint, if the intersection is non-empty, there is no reason to be suspicious. However, from the practical viewpoint, if the intersection is too narrow - i.e., almost empty - then we should also be suspicious, and mark this as an possible additional outlier case. In this paper, we describe a natural way to formalize this idea, and an algorithm for detecting such additional possible outliers.

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How to Detect Possible Additional Outliers: Case of Interval Uncertainty. / Dbouk, Hani; Schön, Steffen; Neumann, Ingo et al.
in: Reliable Computing, Jahrgang 28, 06.2021, S. 100-106.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Dbouk, H, Schön, S, Neumann, I & Kreinovichy, V 2021, 'How to Detect Possible Additional Outliers: Case of Interval Uncertainty', Reliable Computing, Jg. 28, S. 100-106. <https://www.cs.utep.edu/vladik/2020/tr20-67b.pdf>
Dbouk H, Schön S, Neumann I, Kreinovichy V. How to Detect Possible Additional Outliers: Case of Interval Uncertainty. Reliable Computing. 2021 Jun;28:100-106.
Dbouk, Hani ; Schön, Steffen ; Neumann, Ingo et al. / How to Detect Possible Additional Outliers : Case of Interval Uncertainty. in: Reliable Computing. 2021 ; Jahrgang 28. S. 100-106.
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AU - Schön, Steffen

AU - Neumann, Ingo

AU - Kreinovichy, Vladik

N1 - Funding information: This work was supported by the German Research Foundation (DFG) as a part of the Research Training Group i.c.sens (grant GRK2159) and by the Institute of Geodesy of the Leibniz University of Hannover. It was also supported in part by the US National Science Foundation grants 1623190 (A Model of Change for Preparing a New Generation for Professional Practice in Computer Science) and HRD-1242122 (Cyber-ShARE Center of Excellence). This paper was written when V. Kreinovich was visiting Leibniz University of Hannover. The authors are grateful to the anonymous referees for valuable suggestions.

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