Resampling Methods for Triangular and Trapezoidal Fuzzy Numbers
Calculate Bertoluzza's (mid/spread) distance for triangular and trapez...
Calculation of the ambiguity for triangular and trapezoidal fuzzy numb...
Calculation of the left-hand ambiguity for triangular and trapezoidal ...
Calculation of the right-hand ambiguity for triangular and trapezoidal...
Calculation of the expected value for triangular and trapezoidal fuzzy...
Calculation of the fuzziness for triangular and trapezoidal fuzzy numb...
Calculation of the value for triangular and trapezoidal fuzzy numbers
Calculation of the width for triangular and trapezoidal fuzzy numbers
Classical bootstrap procedure for triangular and trapezoidal fuzzy num...
Comparison of the resampling approaches based on the power for the one...
Comparison of the resampling approaches based on the power for the one...
Comparison of the resampling approaches based on the SE/MSE for the me...
d method for resampling triangular and trapezoidal fuzzy numbers
E(xpected value)W(idth) resampling method for triangular and trapezoid...
FuzzyResampling: Resampling Methods for Triangular and Trapezoidal Fuz...
Generate initial sample using various random distributions.
Generate initial sample using the normal and uniform distributions.
Generate initial sample using the normal and uniform distributions.
Calculate p-value of the one-sample test for the mean
A vector containing names of all resampling methods.
A vector containing names of all sampling generators
Calculate SE/MSE for the mean of the bootstrapped samples.
Calculate p-value of the two-sample test for the mean
V(alue)A(mbiguity, left-hand)A(mbiguity, right-hand) resampling method...
V(alue)A(mbiguity)F(uzziness) resampling method for triangular and tra...
V(alue)A(mbiguity) resampling method for triangular and trapezoidal fu...
w method for resampling triangular and trapezoidal fuzzy numbers
The classical (i.e. Efron's, see Efron and Tibshirani (1994, ISBN:978-0412042317) "An Introduction to the Bootstrap") bootstrap is widely used for both the real (i.e. "crisp") and fuzzy data. The main aim of the algorithms implemented in this package is to overcome a problem with repetition of a few distinct values and to create fuzzy numbers, which are "similar" (but not the same) to values from the initial sample. To do this, different characteristics of triangular/trapezoidal numbers are kept (like the value, the ambiguity, etc., see Grzegorzewski et al. <doi:10.2991/eusflat-19.2019.68>, Grzegorzewski et al. (2020) <doi:10.2991/ijcis.d.201012.003>, Grzegorzewski et al. (2020) <doi:10.34768/amcs-2020-0022>, Grzegorzewski and Romaniuk (2022) <doi:10.1007/978-3-030-95929-6_3>, Romaniuk and Hryniewicz (2019) <doi:10.1007/s00500-018-3251-5>). Some additional procedures related to these resampling methods are also provided, like calculation of the Bertoluzza et al.'s distance (aka the mid/spread distance, see Bertoluzza et al. (1995) "On a new class of distances between fuzzy numbers") and estimation of the p-value of the one- and two- sample bootstrapped test for the mean (see Lubiano et al. (2016, <doi:10.1016/j.ejor.2015.11.016>)). Additionally, there are procedures which randomly generate trapezoidal fuzzy numbers using some well-known statistical distributions.
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