Flexible Bootstrap for Fuzzy Data Based on the Canonical Representation
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- DOI
- 10.2991/ijcis.d.201012.003How to use a DOI?
- Keywords
- Ambiguity; Bootstrap; Canonical representation; Fuzziness; Fuzzy data; Fuzzy numbers; Random fuzzy numbers; Resampling
- Abstract
Several new resampling methods for generating bootstrap samples of fuzzy numbers are proposed. To avoid undesired repetitions in the secondary samples we do not draw randomly directly observations from the primary samples but construct them allowing for some modifications in their membership functions, however only such which do not disturb the canonical representation of the initial fuzzy numbers. We consider both two-parameter and three-parameter canonical representations, as well as the triangular and trapezoidal outputs in the secondary samples. Numerical experiments concerning some statistical tests based on fuzzy samples show that the suggested methods may appear helpful in statistical reasoning with imprecise data.
- Copyright
- © 2020 The Authors. Published by Atlantis Press B.V.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - Przemyslaw Grzegorzewski AU - Olgierd Hryniewicz AU - Maciej Romaniuk PY - 2020 DA - 2020/10/21 TI - Flexible Bootstrap for Fuzzy Data Based on the Canonical Representation JO - International Journal of Computational Intelligence Systems SP - 1650 EP - 1662 VL - 13 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.201012.003 DO - 10.2991/ijcis.d.201012.003 ID - Grzegorzewski2020 ER -