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
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.
Organisation(s): Geodetic Institute
Institute of Geodesy
Leibniz Research Centre FZ:GEO
External Organisation(s): University of Texas at El Paso
Type: Contribution to book/anthology
Pages: 371-381
No. of pages: 11
Publication date: 30.09.2017
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Control and Systems Engineering, General Computer Science
Electronic version(s): https://doi.org/10.1007/978-3-319-67137-6_41 (Access: Closed)
: Details in the research portal "Research@Leibniz University"