<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Fuzzy numbers are often used for modeling imprecise perceptions of the real-valued observations. Such epistemic fuzzy data may cause problems in statistical reasoning and data analysis. We propose a universal nonparametric technique, called the epistemic bootstrap, which could be helpful when the existing methods do not work or do not give satisfactory results. Besides the simple epistemic bootstrap, we develop its several refinements that aim to reduce the variance in statistical inference. We also perform an extended simulation study to examine statistical properties of the approaches considered. The discussion of the results is supplemented by some hints for practical use.</p>
</abstract>

Fuzzy numbers are often used for modeling imprecise perceptions of the real-valued observations. Such epistemic fuzzy data may cause problems in statistical reasoning and data analysis. We propose a universal nonparametric technique, called the epistemic bootstrap, which could be helpful when the existing methods do not work or do not give satisfactory results. Besides the simple epistemic bootstrap, we develop its several refinements that aim to reduce the variance in statistical inference. We also perform an extended simulation study to examine statistical properties of the approaches considered. The discussion of the results is supplemented by some hints for practical use.

Bootstrap Methods for Epistemic Fuzzy Data

Faculty of Mathematics and Information Science

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Bootstrap Methods for Epistemic Fuzzy Data"}

Fuzzy numbers are often used for modeling imprecise perceptions of the real-valued observations. Such epistemic fuzzy data may cause problems in statistical...

Bootstrap Methods for Epistemic Fuzzy Data

Online veröffentlicht: 04. Juli 2022

Seitenbereich: 285 - 297

Eingereicht: 18. Okt. 2021

Akzeptiert: 20. Apr. 2022

DOI: https://doi.org/10.34768/amcs-2022-0021

Schlüsselwörter
bootstrap, estimation, fuzzy data, fuzzy numbers, hypotheses testing, resampling

© 2022 Przemysław Grzegorzewski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Bootstrap Methods for Epistemic Fuzzy Data

Online veröffentlicht: 04. Juli 2022

Seitenbereich: 285 - 297

Eingereicht: 18. Okt. 2021

Akzeptiert: 20. Apr. 2022

DOI: https://doi.org/10.34768/amcs-2022-0021

Schlüsselwörterbootstrap, estimation, fuzzy data, fuzzy numbers, hypotheses testing, resampling

© 2022 Przemysław Grzegorzewski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
bootstrap, estimation, fuzzy data, fuzzy numbers, hypotheses testing, resampling