Simulation and Resampling Methods for Epistemic Fuzzy Data
Simulate random fuzzy number.
Simulate a sample of random fuzzy numbers.
Generate a bootstrap sample with the epistemic antithetic bootstrap.
Apply averaging statistics epistemic test for one or two fuzzy samples...
Combine several p-values to obtain a single value.
Generate a bootstrap sample with the epistemic bootstrap.
Calculate the corrected variance using the epistemic bootstrap.
Apply the epistemic bootstrap to find an estimator.
Estimate the mean using the epistemic bootstrap.
Apply epistemic test for one or two fuzzy samples.
A simple wrapper to provide the p-value for the K-S one- and two-sampl...
Apply multi-statistic epistemic test for one or two fuzzy samples.
Apply resampling epistemic test for one or two fuzzy samples.
Random simulations of fuzzy numbers are still a challenging problem. The aim of this package is to provide the respective procedures to simulate fuzzy random variables, especially in the case of the piecewise linear fuzzy numbers (PLFNs, see Coroianua et al. (2013) <doi:10.1016/j.fss.2013.02.005> for the further details). Additionally, the special resampling algorithms known as the epistemic bootstrap are provided (see Grzegorzewski and Romaniuk (2022) <doi:10.34768/amcs-2022-0021>, Grzegorzewski and Romaniuk (2022) <doi:10.1007/978-3-031-08974-9_39>, Romaniuk et al. (2024) <doi:10.32614/RJ-2024-016>) together with the functions to apply statistical tests and estimate various characteristics based on the epistemic bootstrap. The package also includes real-life datasets of epistemic fuzzy triangular and trapezoidal numbers. The fuzzy numbers used in this package are consistent with the 'FuzzyNumbers' package.