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

<p>Epistemic bootstrap is a resampling algorithm that generates bootstrap real-valued samples based on some epistemic fuzzy data input. We apply this method as a universal basis for various statistical tests which can be then directly used for fuzzy random variables. Two classical goodness-of-fit tests are considered as an example to examine the suggested methodology for both synthetic and real data. The proposed approach is also compared with two other goodness-of-fit tests dedicated directly to fuzzy data.</p>
</abstract>

Epistemic bootstrap is a resampling algorithm that generates bootstrap real-valued samples based on some epistemic fuzzy data input. We apply this method as a universal basis for various statistical tests which can be then directly used for fuzzy random variables. Two classical goodness-of-fit tests are considered as an example to examine the suggested methodology for both synthetic and real data. The proposed approach is also compared with two other goodness-of-fit tests dedicated directly to fuzzy data.

Bootstrapped Tests for Epistemic Fuzzy Data

Faculty of Mathematics and Information Science, Warsaw University of Technology

International Journal of Applied Mathematics and Computer Science

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

{"article-title":"Bootstrapped Tests for Epistemic Fuzzy Data"}

Epistemic bootstrap is a resampling algorithm that generates bootstrap real-valued samples based on some epistemic fuzzy data input. We apply this method as a...

Bootstrapped Tests for Epistemic Fuzzy Data

Published Online: Jun 25, 2024

Page range: 277 - 289

Received: Jul 26, 2023

Accepted: Jan 17, 2024

DOI: https://doi.org/10.61822/amcs-2024-0020

Keywords
bootstrap, fuzzy data, nonparametric statistics, simulation, statistical computing

© 2024 Przemysław Grzegorzewski et al., published by Sciendo

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

Bootstrapped Tests for Epistemic Fuzzy Data

Published Online: Jun 25, 2024

Page range: 277 - 289

Received: Jul 26, 2023

Accepted: Jan 17, 2024

DOI: https://doi.org/10.61822/amcs-2024-0020

Keywordsbootstrap, fuzzy data, nonparametric statistics, simulation, statistical computing

© 2024 Przemysław Grzegorzewski et al., published by Sciendo

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

Keywords
bootstrap, fuzzy data, nonparametric statistics, simulation, statistical computing