Bootstrapped tests for epistemic fuzzy data - Grzegorzewski, Przemysław; Romaniuk, Maciej (Zielona Góra: Uniwersytet Zielonogórski)
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