Bootstrapped Tests for Epistemic Fuzzy Data

Przemysław Grzegorzewski; Maciej Romaniuk

doi:10.61822/amcs-2024-0020

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Bootstrapped Tests for Epistemic Fuzzy Data

International Journal of Applied Mathematics and Computer Science

Volume 34 (2024): Issue 2 (June 2024)

By: Przemysław Grzegorzewski and Maciej Romaniuk

Open Access

|Jun 2024

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.

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Articles in this issue

DOI: https://doi.org/10.61822/amcs-2024-0020 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X

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Language: English

Page range: 277 - 289

Submitted on: Jul 26, 2023

Accepted on: Jan 17, 2024

Published on: Jun 25, 2024

Published by: University of Zielona Góra

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

bootstrap,

fuzzy data,

nonparametric statistics,

simulation,

statistical computing

Related subjects:

Mathematics,

Applied mathematics

© 2024 Przemysław Grzegorzewski, Maciej Romaniuk, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Volume 34 (2024): Issue 2 (June 2024)