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Paper details
Number 2 - June 2024
Volume 34 - 2024
Bootstrapped tests for epistemic fuzzy data
Przemysław Grzegorzewski, Maciej Romaniuk
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.
Keywords
bootstrap, fuzzy data, nonparametric statistics, simulation, statistical computing