Flexible resampling for fuzzy data
In this paper, a new methodology for simulating bootstrap samples of fuzzy numbers is proposed. Unlike the classical bootstrap, it allows enriching a resampling scheme with values from outside the initial sample. Although a secondary sample may contain results beyond members of the primary set, they...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Sciendo
2020-06-01
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| Series: | International Journal of Applied Mathematics and Computer Science |
| Subjects: | |
| Online Access: | https://doi.org/10.34768/amcs-2020-0022 |
| _version_ | 1818595099694596096 |
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| author | Grzegorzewski Przemyslaw Hryniewicz Olgierd Romaniuk Maciej |
| author_facet | Grzegorzewski Przemyslaw Hryniewicz Olgierd Romaniuk Maciej |
| author_sort | Grzegorzewski Przemyslaw |
| collection | DOAJ |
| description | In this paper, a new methodology for simulating bootstrap samples of fuzzy numbers is proposed. Unlike the classical bootstrap, it allows enriching a resampling scheme with values from outside the initial sample. Although a secondary sample may contain results beyond members of the primary set, they are generated smartly so that the crucial characteristics of the original observations remain invariant. Two methods for generating bootstrap samples preserving the representation (i.e., the value and the ambiguity or the expected value and the width) of fuzzy numbers belonging to the primary sample are suggested and numerically examined with respect to other approaches and various statistical properties. |
| first_indexed | 2024-12-16T11:10:38Z |
| format | Article |
| id | doaj.art-3a441e99ce6149fea5af99bf3e103960 |
| institution | Directory Open Access Journal |
| issn | 2083-8492 |
| language | English |
| last_indexed | 2024-12-16T11:10:38Z |
| publishDate | 2020-06-01 |
| publisher | Sciendo |
| record_format | Article |
| series | International Journal of Applied Mathematics and Computer Science |
| spelling | doaj.art-3a441e99ce6149fea5af99bf3e1039602022-12-21T22:33:44ZengSciendoInternational Journal of Applied Mathematics and Computer Science2083-84922020-06-0130228129710.34768/amcs-2020-0022amcs-2020-0022Flexible resampling for fuzzy dataGrzegorzewski Przemyslaw0Hryniewicz Olgierd1Romaniuk Maciej2Systems Research Institute, Polish Academy of Sciences,Newelska 6, 01-447Warsaw, PolandSystems Research Institute, Polish Academy of Sciences,Newelska 6, 01-447Warsaw, PolandSystems Research Institute, Polish Academy of Sciences,Newelska 6, 01-447Warsaw, PolandIn this paper, a new methodology for simulating bootstrap samples of fuzzy numbers is proposed. Unlike the classical bootstrap, it allows enriching a resampling scheme with values from outside the initial sample. Although a secondary sample may contain results beyond members of the primary set, they are generated smartly so that the crucial characteristics of the original observations remain invariant. Two methods for generating bootstrap samples preserving the representation (i.e., the value and the ambiguity or the expected value and the width) of fuzzy numbers belonging to the primary sample are suggested and numerically examined with respect to other approaches and various statistical properties.https://doi.org/10.34768/amcs-2020-0022bootstrapfuzzy datafuzzy numbersfuzzy sampleimprecise dataresampling |
| spellingShingle | Grzegorzewski Przemyslaw Hryniewicz Olgierd Romaniuk Maciej Flexible resampling for fuzzy data International Journal of Applied Mathematics and Computer Science bootstrap fuzzy data fuzzy numbers fuzzy sample imprecise data resampling |
| title | Flexible resampling for fuzzy data |
| title_full | Flexible resampling for fuzzy data |
| title_fullStr | Flexible resampling for fuzzy data |
| title_full_unstemmed | Flexible resampling for fuzzy data |
| title_short | Flexible resampling for fuzzy data |
| title_sort | flexible resampling for fuzzy data |
| topic | bootstrap fuzzy data fuzzy numbers fuzzy sample imprecise data resampling |
| url | https://doi.org/10.34768/amcs-2020-0022 |
| work_keys_str_mv | AT grzegorzewskiprzemyslaw flexibleresamplingforfuzzydata AT hryniewiczolgierd flexibleresamplingforfuzzydata AT romaniukmaciej flexibleresamplingforfuzzydata |