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Bootstrapped Tests for Epistemic Fuzzy Data

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.

article Article
date_range 2024
language English
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Featured Keywords

bootstrap
fuzzy data
nonparametric statistics
simulation
statistical computing
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