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Taking into Account Interval (and Fuzzy) Uncertainty Can Lead to More Adequate Statistical Estimates

verfasst von
Ligang Sun, Hani Dbouk, Ingo Neumann, Steffen Schön, Vladik Kreinovich
Abstract

Traditional statistical data processing techniques (such as Least Squares) assume that we know the probability distributions of measurement errors. Often, we do not have full information about these distributions. In some cases, all we know is the bound of the measurement error; in such cases, we can use known interval data processing techniques. Sometimes, this bound is fuzzy; in such cases, we can use known fuzzy data processing techniques. However, in many practical situations, we know the probability distribution of the random component of the measurement error and we know the upper bound on the measurement error’s systematic component. For such situations, no general data processing technique is currently known. In this paper, we describe general data processing techniques for such situations, and we show that taking into account interval and fuzzy uncertainty can lead to more adequate statistical estimates.

Organisationseinheit(en)
Geodätisches Institut
Institut für Erdmessung
Externe Organisation(en)
University of Texas at El Paso
Typ
Beitrag in Buch/Sammelwerk
Seiten
371-381
Anzahl der Seiten
11
Publikationsdatum
30.09.2017
Publikationsstatus
Veröffentlicht
Peer-reviewed
Ja
ASJC Scopus Sachgebiete
Steuerungs- und Systemtechnik, Informatik (insg.)
Elektronische Version(en)
https://doi.org/10.1007/978-3-319-67137-6_41 (Zugang: Unbekannt)
 

Details im Forschungsportal „Research@Leibniz University“