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

authored by
Ligang Sun, Hani Dbouk, Ingo Neumann, Steffen Schön, Vladik Kreinovich

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.

Geodetic Institute
Institute of Geodesy
Leibniz Research Centre FZ:GEO
External Organisation(s)
University of Texas at El Paso
Contribution to book/anthology
No. of pages
Publication date
Publication status
Peer reviewed
ASJC Scopus subject areas
Control and Systems Engineering, Computer Science(all)
Electronic version(s)
https://doi.org/10.1007/978-3-319-67137-6_41 (Access: Closed)

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