FUZZINESS AND STATISTICS – MATHEMATICAL MODELS FOR UNCERTAINTY

Owat Sunanta () and Reinhard Viertl ()
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Owat Sunanta: Institute of Statistics and Mathematical Methods in Economics, Technische Universität Wien, Austria, Postal: AT
Reinhard Viertl: Institute of Statistics and Mathematical Methods in Economics, Technische Universität Wien, Austria, Postal: AT

Theoretical and Practical Research in the Economic Fields, 2017, vol. 8, issue 1, 31-46

Abstract: Real data from continuous quantities, considered under different models in economic theory, cannot be measured precisely. As a result, measurement results cannot be accurately represented by real numbers, as they contain different kinds of uncertainty. Beside errors and variability, individual measurement results are more or less fuzzy as well. Therefore, real data have to be described mathematically in an adequate way. The best up-to-date models for this are so-called fuzzy numbers, which are special fuzzy subsets of the set of real numbers. Based on this description, statistical analysis methods must be generalized to the situation of fuzzy data. This is possible and will be explained here for descriptive statistics, inferential statistics, objective statistics, and Bayesian inference.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:srs:jtpref:v:8:y:2017:i:1:p:31-46

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