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
Leibniz Forschungszentrum FZ:GEO
- 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:
Geschlossen)
